personal choice research paper

Perceived support and influences in adolescents’ career choices: a mixed-methods study

• Open access
• Published: 02 September 2023

Cite this article

You have full access to this open access article

• Jenny Marcionetti   ORCID: orcid.org/0000-0001-7906-8785 1 &
• Andrea Zammitti 2  

3523 Accesses

2 Citations

1 Altmetric

Explore all metrics

Support and influences on adolescents’ career choices come from a variety of sources. However, studies comparing the importance given to various sources of support are few, and none have analyzed differences in the support provided by mothers and fathers. This study aimed to examine quantitatively the importance given to support from various sources in a sample of 432 Swiss adolescents at two points in time in the period of choice and to explore qualitatively experiences related to support given/received by 10 mother–child dyads in the career choice process. The overall results endorse the mother as the main source of support.

Similar content being viewed by others

Associations between social support, career self-efficacy, and career indecision among youth

Reciprocal effects between parental support and career maturity in the developmental process of career maturity

Middle school students’ career parental support and adolescent–parent career congruence: the mediating role of self-efficacy

Avoid common mistakes on your manuscript.

With the arrival of adolescence, career planning becomes very important (Gati & Saka, 2001 ). Among the main difficulties that adolescents have to overcome, there are school–professional choices (Lodi et al., 2008 ). In fact, around the age of 14–15 years, adolescents must make choices about their future and can live a condition of indecision and insecurity that is associated with difficulties in making decisions and with procrastination or avoidance of the choice task (Nota & Soresi, 2002 ). This process is certainly not facilitated by the 21st-century context, in which it is increasingly difficult to make predictions, ask for suggestions, or choose (Soresi & Nota, 2015 ).

It is widely recognized that parental support plays a fundamental role in career development of sons and daughters (Whiston & Keller, 2004 ), and in particular the support provided by mothers (Colarossi & Eccles, 2003 ; Furman & Buhrmester, 1992 ; Levitt et al., 1993 ). School actors, principally teachers, have also been found to be an important source of support for career choices (Wong et al., 2021 ). Although various studies agree on the importance of adolescents perceiving social support to deal with the career choice process (Whiston & Keller, 2004 ), still few have been interested in understanding what the most important sources of this support are (e.g. Cheung & Arnold, 2010 ; Gushue & Whitson, 2006 ), distinguishing not only between parents, school guidance counselors, and teachers but also between mothers and fathers, and also investigating whether the adolescent’s gender might influence this perception. In addition, few have studied adolescents’ perceptions of influences they have had and support received relating to the career choice and at the same time their parents’ perceptions of influence and support provided using a qualitative approach.

The present study thus had two main aims, each pursued with a specific approach. The first, using a quantitative approach, was to examine the importance of different sources of support in a sample of adolescents at two points in time in their last year of compulsory school. The second, with a qualitative approach, was to delve into the experience related to support given/received by 10 mother–child dyads in the career choice process.

Parental influences

Parents are a major source of interpersonal support and can influence their children’s self-efficacy and professional expectations, their interests, and career goals (Kenny & Medvide, 2013 ). It has been shown that adolescents consider it normal to be influenced by their parents in career choices and do not think that decisions will be made only by them (Bernardo, 2010 ). Indeed, the expectations of parents contribute to obtaining positive career outcomes (Fouad et al., 2008 ). However, this is valid only when the adolescent believes he can meet these expectations (Leung et al., 2011 ); when the adolescent does not feel up to meeting the expectations of parents, there is the risk of developing psychological distress (Wang & Heppner, 2002 ). Hence, parental support in this area can foster aspirations, exploration, and career planning (Cheung & Arnold, 2010 ; Ma & Yeh, 2010 ) but as long as it is actually perceived as support (Garcia et al., 2012 ).

Career concerns have to do with the stress of planning a future task (Cairo et al., 1996 ; Savickas et al., 1988 ). They represent anxiety about the fact that the individual is managing something important for their professional future (Code & Bernes, 2006 ). Students who find themselves making a choice must deal with this stress and manage the choice also based on the expectations of family, peers, and educational institutions (Creed et al., 2009 ). The career choice process, therefore, implicates emotions (Blustein et al., 1995 ) that involve both the adolescent and their parents. These emotions can stimulate action and make sense of the career development process within the family setting (Young et al., 1997 ). However, they can also be associated with prolonged indecision (Gati et al., 2011 ) and make mothers overly concerned, especially when adolescents hardly discuss their future plans (Kobak et al., 1994 ). Indeed, the behavior of parents concerning the choices of their children can be support, when they help them make choices by providing them with guidance, but also interference, when they excessively control the choices their children make, or lack of engagement, due to disinterest or other factors such as financial problems, overwork, or other (Dietrich & Kracke, 2009 ).

What has been expressed up to now indicates that parents can be a valid resource that provides instrumental and emotional support to the adolescent in transition increasing self-efficacy in career decision-making (Lent et al., 2003 ), professional and career adaptability (Kenny & Bledsoe, 2005 ; Parola & Marcionetti, 2021 ), career exploration (Kracke, 1997 ), and diminishing indecision about career choices (Guerra & Braungart-Rieker, 1999 ; Marcionetti & Rossier, 2016a ; Parola & Marcionetti, 2021 ). On the other hand, they can also constitute a risk factor in career choices when they interfere too much or lack engagement in this process (Dietrich & Kracke, 2009 ; Zhao et al., 2012 ).

Colarossi and Eccles ( 2003 ) conducted a study considering the perception of parental support on adolescents in the development of self-esteem or depression; the peculiarity of this study is that parental support was distinguished in support received from fathers and support received from mothers. Indeed, a limitation of research on parental support is that it is often considered as a single measure, without separating maternal and paternal support and considering the gender of the adolescent. The authors have shown, in fact, that the effects are different for male and female adolescents. In particular, male adolescents perceive greater support from fathers than females whereas it has been found that there are no significant differences concerning the perception of the support received from the mothers. Finally, fathers, compared to mothers, teachers and peers were perceived as providers of a smaller amount of support. This study is consistent with other research carried out in this area (Colarossi, 2001 ; Furman & Buhrmester, 1992 ; Levitt et al., 1993 ) and underlines the idea that it is important to differentiate maternal and paternal support. Indeed, according to Leaper et al. ( 1998 ), mothers show a tendency to use more supportive language than fathers and are more involved when it comes to the school and educational decisions of their children. According to this, Ginevra et al. ( 2015 ) and Porfeli et al. ( 2013 ) showed that mothers perceive themselves as more supportive than fathers in the career development of their children. Other authors also confirm these results, which underlines the greater role of mothers compared with fathers in their children’s career choices (e.g., McCabe & Barnett, 2000 ).

School influences

In middle schools, school counselors and career guidance specialists are often the main personnel responsible for monitoring and helping students in sustaining the career choice (Gysbers & Lapan, 2009 ; Multon, 2006 ). This is also the case in Southern Switzerland, where this study has been conducted. However, in studies made in other countries, it emerged that students do not always see their services as sufficient, or helpful (Mortimer et al., 2002 ). Hence, in many countries more responsibility has been given to teachers for supporting their students’ career development. On the one hand, teachers can give “general support” that can promote the development of different career and life competencies during their classes (Kivunja, 2014 ). In this sense, Lei et al. ( 2018 ) say that teachers provide general support in both giving social support, which involves emotional, instrumental, informational, and appraisal support, and promoting self-determination. Indeed, the teacher supports the development of autonomy, decision-making, and intrinsic motivation, which increase in adolescents the motivation to pursue life and career goals. On the other hand, teachers can give specific career-related support. For Wong et al. ( 2021 ) specific career-related support is “anything a teacher does that can facilitate the career planning of students” (Wong et al., 2021 , p. 132) such as inquiring about career paths, helping students identify their interests, giving information about jobs, and providing help in setting goals. Teacher support has been proven to have a significant impact on the development of students’ career aspirations, future orientation, career exploration, and planning (Alm et al., 2019 ; Hirschi et al., 2011 ; Rogers & Creed, 2011 ).

Studies that have compared the importance of various sources of support in the career decision-making process seem to point to teachers as the most important source of support (although the differences are never huge). These studies are few in number and have been conducted on quite diverse samples in terms of culture and age and considering different career-related outcomes. For example, Gushue and Whitson ( 2006 ) in the USA have shown that teachers support has more effect than parental support on the level of African American ninth-grade public high school students’ positive expectations about the career chosen. The study from Di Fabio and Kenny ( 2015 ) with Italian high school students suggests that teacher support contributes more than peer support in increasing resilience, perceived employability, and self-efficacy. Cheung and Arnold ( 2010 ) found that teacher support is more effective than parental and peers’ support in predicting career exploration in Hong Kong university students. Finally, Kenny and Bledsoe ( 2005 ) in a sample of US urban high school students showed that support from family, teachers, close friends, and peer beliefs about school all contributed significantly to the explanation of the four dimensions of career adaptability, school identification, perceptions of educational barriers, career outcome expectations, and career planning. Moreover, they analyzed the different contribution of each relational variable when controlling for the others, finding that family support contributed to explaining variance in perceived educational barriers and career expectations; teacher support contributed to explaining variance in school identification; and perceived peer beliefs contributed to explaining perceived educational barriers and school identification. The results thus seem to indicate that different actors may contribute differently to support the choice process. This suggests that all actors can play an important role in providing support. However, no study to our knowledge has captured adolescents’ perceptions with respect to which figure has been most supportive in this process. It is indeed important that not only does the support offered have a concrete effect on the choice process, but also that it is recognized, otherwise risking being interpreted as “lack of engagement,” and positively valued by the adolescent, otherwise risking being interpreted as “interference” (Dietrich & Kracke, 2009 ).

Methodologies to study career-related social support

Social support can be provided by close relatives such as parents and siblings and by other persons more or less trained to give it, such as career counselors and teachers. It can also be of different types; for instance, Cutrona and Russell ( 1990 ) and Cutrona ( 1996 ) distinguished between emotional support (the support given through love and empathy, concern, comfort, and security), social integration or network support (the support given by the feeling part of a group with people who hold similar interests and concerns), esteem support (the support that boosts others self-confidence through respect for others qualities, belief in another’s abilities, and validation of thoughts, feelings, or actions), information support (the factual input, advice, or guidance and appraisal of the situation), and tangible assistance (the support through instrumental assistance with tasks or resources). Moreover, the support received, and then perceived, can be influenced by one’s tendency and ability to ask for it (Marcionetti & Rossier, 2016b ). The same goes for the ability to give support and then to feel efficient in giving it. Finally, all these aspects can be influenced by cultural differences (Ishii et al., 2017 ).

Despite the complex nature of social support, there are few studies in which adolescents were asked who the most important people were in providing support in the process of school and career choice by directly asking them for their opinion on the matter. As mentioned earlier, we believe it is important that the support offered (by parents, school and career counselor, teacher, or peers) is also perceived and evaluated positively, lest it instead be deemed lacking in engagement or experienced as interference (Dietrich & Kracke, 2009 ). Moreover, the studies carried out to investigate the importance of different sources of career-related support for adolescents are mostly quantitative in nature, although there are some exceptions (e.g., Schultheiss et al., 2001 ; Young & Friesen, 1992 ). There is also a lack of studies investigating how parents–child relationships are influential in the career development process and the parents and children’s cross-perceptions of them and of the emotionality felt in them.

An interesting way to fill some of these research gaps is to use a mixed-methods research design. What is unique about these methods is that they allow both quantitative and qualitative approaches to be used in a single research study or set of related studies. To do this there are various ways that can make one or the other of these approaches precede the other and give different or equal importance to them (Creswell & Creswell, 2017 ; Stick & Lincoln, 2006 ). For example, in a first phase, quantitative data can be collected through the administration of a questionnaire. Then, the descriptive data provided in this phase of the study can be used to guide the subsequent qualitative data collection with face-to-face interviews. Thus, mixed method research utilizes a quantitative and qualitative approach to create a stronger research result than either method individually (Malina et al., 2011 ).

Quantitative data collected by sample-administered questionnaires and analyzed by the well-known statistical methods allow generalizability of collected data to the broader population (Creswell & Creswell, 2017 ). Instead, semi-structured interviews are a useful qualitative method to explore perceptions, experiences, and ideas on specific topics (Gill et al., 2008 ; Taylor, 2005 ; Wengraf, 2001 ). Semi-structured interviews have already been used for studies on career development support (e.g., Parola & Marcionetti, 2020 ; Schultheiss et al., 2001 ). To analyze information collected with semi-structured interviews, there are different methods. Content analysis allows making replicable and valid inferences from data to their context (Krippendorff, 1980 ; Mayring, 2000 ). The aim of this approach is to provide knowledge, new insights, and new representations of facts. It implies choosing some categories linked to the research question that are used to analyze a conversation or a text. This method has the advantage of permitting the identification of the main themes contained in a message and the way the message is expressed. However, it has the disadvantage of being quite sensitive to the researcher’s aims and corpus of data (Tomasetto & Selleri, 2004 ). Another useful method of analysis is thematic analysis that allows identifying, organizing, and explaining themes in a dataset (Braun & Clarke, 2012 ). It is simple to use, flexible, and allows anyone to easily read the results, enabling social and psychological interpretations of the data (Braun & Clarke, 2006 ; Javadi & Zarea, 2016 ). For these reasons, thematic analysis is one of the most common methods of analysis in qualitative research (Guest et al., 2012 ). If mixed approaches (combining quantitative and qualitative approaches) for data collection are becoming more popular, few studies have so far combined content and textual analyses of interviews (e.g., Zambelli et al., 2020 ).

Aims of the study and methodological approach for data collection

Given that studies seem to indicate that (a) there is a differential perception of support providers between boys and girls, and (b) teachers are important support providers (Wong et al., 2021 ), in some cases even more effective than parents (e.g. Cheung & Arnold, 2010 ) and school guidance counselors (e.g. Gysbers & Lapan, 2009 ; Multon, 2006 ), the first aim of this study was to understand, from the point of view of adolescents, which are the main providers of support for career decision-making process at the end of compulsory school (at the beginning and at the end of the last year) and what are the eventual differences in perceptions between boys and girls. Few studies have considered the differences between mother and father, which is why they were given importance in this study. The specific research question guiding this part of the study was thus “which sources of support are most influential in career exploration and decision-making of adolescents?”. We felt it necessary that the answer to this question could be generalized to a large population of adolescents to be sure to focus the qualitative part of the study on the most important actors and content. Hence, this question was divided into sub-questions which then formed the questionnaire submitted to the adolescents. It was in fact considered important to explore: (a) whether the students had used the guidance service offered at school; (b) whether they had gone to the school and vocational guidance counsellor alone or accompanied (and by whom); (c) whether they were helped by someone in the choice process inside or outside school; (d) whether they felt they needed further help in choosing a school or a profession for the following year; and (e) who they considered to be the main source of support for this choice. The questionnaire was administered at two points in time, at the beginning and at the end of the last compulsory school year, to take into account and control for eventual variations in support perception at two important moments of the career choice process. In fact, for some teenagers, the summer period before the start of the school year is an opportunity to test some choices with company internships and meetings with potential employers. For others, the most important period is placed later in the school year, since the possibility of accessing some schools depends on academic success.

Based on the results that emerged in this first phase of the study, we considered that many studies have been published about the theme of the influence of parents on children, but that it is not always easy to trace the depth of a parent’s influence on a son or daughter’s career choices (Whiston & Keller, 2004 ). In fact, most of the previous studies have investigated the influences of parents on the career development of children in a quantitative way or have taken into consideration only the point of view of the children, mainly, or that of the parents. The second aim of this study was therefore to explore this aspect with a qualitative approach by involving 10 mother–child dyads to explore their possibly different points of view and emotionality. The choice to consider only mothers in this second qualitative part of the study was made bearing in mind the results obtained in the first quantitative part of the study. However, the goodness of this choice was also underpinned by the adolescents contacted for this second phase: when asked for a parental contact to discuss the topic of school and career choice, all spontaneously provided their mother’s. The two main specific research questions guiding this second part of the study were thus “what role do parents play in their child’s career decisions from the perspective of the mothers and of the children?” and “what sentiments emerge during the career decision-making process in mothers and children?”. Specific information about the participants involved in the two phases of the study, how they were involved, and the procedures for data collection and analysis will be laid out in the next section.

Participants

The study was conducted in southern Switzerland, in the Swiss Canton of Ticino, where the official language is Italian. In southern Switzerland, adolescents aged approximately 14 or 15 years, after middle school must choose between continuing a general education at a high school or starting an apprenticeship that usually involves spending three days at the company and two at a vocational school. Some full-time vocational schools also complete apprenticeship training. This choice can be difficult; indeed, access to high schools and some apprenticeships is limited to only those students with good academic achievement. Moreover, apprenticeships that provide part of the training with a company are accessible only to those adolescents who have found an employer. In this context, social support is crucial. A school guidance counselor is present in the middle school a few days a week and is available for one-on-one meetings with students; they may be accompanied by parents or family members if they wish. At the time of study, teachers have no institutionally defined role in supporting their students’ career choices. Only the class referent teacher, the penultimate and final year of middle school, is responsible for providing them, during class time, with information on the school and career guidance website ( www.orientamento.ch ) or first-hand information on available apprenticeship positions provided by the school guidance counselor.

Hence, students from 7 of 35 middle schools situated in various geographic locations (city, city’s periphery, and schools located in small villages in the valleys) were involved in the first part of the study. There were 432 participants at the two data collections (in October and May of the last year of compulsory school), 224 boys and 208 girls. During each questionnaire administration, the students received information about the aim of the study and were reassured about the confidentiality of their answers. The questionnaires were completed in an IT classroom during an ordinary lesson and under the supervision of the first author.

After the last data collection, 10 pairs of mothers and children for a total of 20 participants were selected to participate in the second part of the study. In the selection of the adolescents, taken from those who participated in the first part of the study and of whom we knew a range of information, some criteria were considered. First, adolescents were selected from two middle schools, a “urban” school and a “valley” school, and at the time of the last quantitative data collection, they provided a telephone number. Second, they had just finished compulsory school and were in the moment of transition between compulsory school and another type of education. Third, the type of education was considered: in the questionnaire, five adolescents said they would enroll in a general high school and five in vocational education and training (VET). Fourth, career decidedness was taken into account: in each school, two adolescents were sure of their educational choice, two had yet to confirm this choice, and one was not sure about it. Fifth, gender was considered: six were girls, and four were boys. Sixth, when reached on the phone, they gave their availability for an interview and provided a parent’s contact information. The parent was contacted and, after explaining the purpose of the study, gave his/her consent and that of their child to participate. We did not specifically ask which parent (mother or father) we wanted to conduct the interview with; however, all 10 adolescents provided the telephone number of the mother, who was also described as the parent principally supporting them in the educational choice and in the moment of transition.

In the questionnaires administrated at the beginning and at the end of the last compulsory school year, students were asked about their gender (masculine/feminine), the middle school in which they were enrolled (multiple choice question, with only one choice possible), about how many meetings with the school counselor they had (exact number requested) and with whom they meet them (multiple choice question, with more than one choice possible), who was helping or helped them make the career choice (multiple choice question, with more than one choice possible), and who helped them the most (multiple choice question, with only one choice possible). In the first questionnaire, students were also asked to indicate whether they felt they need more support (yes/no) and from whom (open question). In the second questionnaire, they were also asked about the type of future career education desired (high school/VET in full time school/VET with apprenticeship), their career decidedness (answer on a scale from 1 = not at all decided to 6 = completely decided), and, after having explained that the study also included interviews with a selection of them and their parents, if they agreed to give it, they provided a telephone number (open question).

Concerning the interviews, mothers and children were met separately at home or in a quiet place that they chose. Both were briefed on the objectives of the research and gave the informed consent to participate. All participants were informed that the data would be processed in aggregate form, without ever mentioning their names. Participants were asked for permission to record the interview. All participants agreed to register. The recording was transcribed and analyses were subsequently carried out. To investigate the areas of interest, semi-structured interviews were used, divided into the following parts: (1) an introduction referring to the description of themselves (or children) and their family, school progress, and relationships with peers, and (2) a section devoted to influences on choice. Semi-structured interviewing is a versatile and flexible data collection method, which can be modified according to the purpose of the research (Kelly, 2010 ). One of the main advantages is that this type of interview allows the interviewer to improvise questions, based on the responses of the participants (Hardon et al., 2004 ; Polit & Beck, 2010 ; Rubin & Rubin, 2005 ). Questions are determined before meeting the interviewee (Rubin & Rubin, 2005 ), to cover the main research topics (Taylor, 2005 ). However, the interview is not followed rigidly and rigorously; the basic idea is to explore the area of ​​interest by providing participants with indications on what to talk about (Gill et al., 2008 ). This makes the semi-structured interview a simple method of data collection (Wengraf, 2001 ). In the present study, the interviews were conducted by the first author, adequately trained to conduct semi-structured interviews, as indicated by the literature (Kelly, 2010 ; Wengraf, 2001 ).

Data analysis

Descriptive analysis of data collected with questionnaires were performed with SPSS. This involved conducting frequency analysis of responses. Since some response categories had low n , especially after dividing them by gender, it was not considered appropriate to carry out more in-depth analyses to see if the differences in response between the first and second data collection were significant, which, moreover, was not an aim of the study.

To analyze the transcriptions of interviews, we used thematic analysis, accompanying this analysis with the use of a software for qualitative analysis (Nvivo 12). This allowed us not only to identify nodes and themes that we considered most relevant but also to show some relationships between them. Thematic analysis is a qualitative analytic method, useful and flexible for psychology research (Braun & Clarke, 2006 ). This method allows the identifying, organizing, and explaining of themes in a dataset (Braun & Clarke, 2012 ). Braun and Clarke ( 2006 ) developed a thematic analysis model divided into six phases: Phase 1: Familiarizing Yourself with the Data. This phase requires that the researcher reads and rereads textual data to highlight items potentially of interest. This phase involves an active reading of the qualitative content, starting to think about the meaning of the data. Phase 2: Generating Initial Codes. Codes represent labels for a data feature, a summary to describe its content, a shortcut that allows the researcher to quickly identify a topic. Phase 3: Searching for Themes. This phase requires the researcher to move from codes to themes. A theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set” (Braun & Clarke, 2006 , p. 82). Phase 4: Reviewing Potential Themes. This phase requires the researcher to review the encoded data and perform a check on the quality of the encodings. Phase 5: Defining and Naming Themes. In a good thematic analysis, the themes should have a clear focus and purpose and be related but not overlapping. Together, the themes provide an overall history of the data. It is possible to have sub-themes within a theme. Phase 6: Producing the Report.

All the interviews were transcribed and then analyzed with the help of the NVivo 12 software. Where necessary, the software made it possible to identify the words most used in the answers, analyze the sentiments with respect to the proposed themes, and identify the differences in the nodes and sentiments between mothers and children. Sentiments are particular nodes divided into positive and negative. Each of these two nodes has two child nodes: a lot and moderately. We also used the NVivo software to highlight the themes we had identified from the thematic analysis. The software also allows you to indicate the attributes (in our case Mother-Son). The assignment of attributes makes it possible to distinguish information about the speaker (whether it is the mother or the child) and consequently to be able to explore the data with subsequent analyses. Specifically, thanks to the assignment of the Mother-Son attributes, we were able to use the matrix coding query that allows you to check how two elements relate to each other. Thanks to the fact that Influences within the Family, External Influences, and Sentiments were coded, we were able to verify how all these codes related to the mother or child attribute (QSR international, 2021 ).

Results from the questionnaires

At the beginning of the ninth grade, 14.4% of adolescents (13.4% of boys; 15.4% of girls) already had a meeting with the school guidance counselor. There were 62 students who had had a meeting; 43.5% of them met the counselor alone (27; 46.7% of boys; 40.6% of girls), and the others in the presence of the mother (32 out of 35 meetings; 50.0% of the meetings of boys, 53.1% of those of girls) and/or the father (7 out of 35 meetings; 10.0% of the meetings of boys, 12.5% of those of girls), or a brother or sister (4 out of 35 meetings; 3.3% of the meetings of boys, 9.3% of those of girls).

A total of 47.9% of the students (207; 47.3% of boys and 48.6% of girls) indicated that no one was helping them making a choice at this moment: 72.5% of them (150) said they did not need help; others (57: 19 boys and 38 girls) cited that, as a possible source of support, they might need their parents (28: 10 boys and 18 girls) and/or the school counselor (21: 8 boys and 13 girls); 13 (4 boys and 9 girls) did not know; and only 3 cited a teacher or “the school” (2 boys and 1 girls). Of the 52.1% of the students who received or were receiving support (225), 91.5% indicated the mother as a source of support (89.8% of the boys and 93.5% of the girls), 75.5% the father (80.5% of the boys and 70.1% of the girls), and 27.5% a brother or sister (28.8% of the boys and 26.2% of the girls). Other relatives or other people were cited by 16% of students, respectively. At the question “from whom, among those people, are you receiving the most important help?,” 64.0% indicated the mother (53.4% of boys and 75.7% of girls), 24.0% the father (34.7% of boys and 12.1% of girls), 6.2% a brother or sister (5.9% of boys and 6.5% of girls), 4.0% other persons (3.4% of boys and 4.7% of girls), 1.3% other relatives (1.7% of boys and 0.9% of girls), and 0.4% their stepmother or stepfather (0.8% of boys and 0.0% of girls).

At the second data collection, i.e., 1 month before the end of compulsory school, 46.8% of adolescents already had a meeting with the school guidance counselor. There were 202 students who had had a meeting; 42.6% met the counselor alone (86; 44.2% boys; 41.1% girls), and the others in the presence of the mother (103 out of 116 meetings; 47.4% of the meetings of boys; 54.2% of those of girls) and/or the father (32 out of 116 meetings; 18.9% of the meetings of boys; 13.1% of those of girls), or a brother or sister (2 out of 116 meetings; 0.01% of the meetings of boys; 0.01% of those of girls).

At this time, 39.1% of the students indicated that no one was helping them make a choice (169; 42.0% of boys and 36.1% of girls). Of the 60.9% of the students stating that someone was helping them (263), 86.6% indicated the mother as a source of support (82.3% among boys and 91.0% among girls), 60.5% the father (68.5% of the boys and 52.6% of the girls), and 20.9% a brother or sister (24.6% of the boys and 17.3% of the girls). Other relatives were cited by 12.9% and other people by 23.2% of students, respectively.

At the question “from whom, among those people, are you receiving the most important help?,” in line with result obtained in the previous data collection, 63.1% indicated the mother (56.2% among boys and 69.9% among girls), 20.5% the father (26.9% among boys and 14.3% among girls), 4.6% a brother or sister (4.6% among boys and 4.5% among girls), 1.2% a stepfather or stepmother (1.6% among boys and 0.8% among girls), 7.6% other persons (7.7% among boys and 7.5% among girls), and 3% other relatives (3.1% among boys and 3.0% among girls).

Results from the interviews

As indicated by Braun and Clarke’s ( 2006 ), we first familiarized ourselves with the data and generated preliminary codes. After, we generated the possible themes and subthemes that were examined and labeled. Finally, we produced the final report. Table 1 shows the established themes and subthemes. We have enriched the table by also indicating the number of times each theme or subtheme appears and how many persons cited it. Two themes emerged and are presented in the following paragraphs. Indeed, the sources of influence on choices were divided into two alternatives: influences and support that come from members of the family (Theme 1) and influences and support that come from outside (Theme 2). For each alternative, a parent-node and some child-nodes were formed and presented.

Theme 1: Influences within the family

Influences within the family were coded as follows: (1) influences from the mother (for example, Claudia, who said, “[my mother] did not oblige me but helped me... according to her I went more that way and she was right ” ), (2) influences from the father’s side (for example, Valeria, who, in response to “who helped you?,” said “Mom and dad”), (3) influences from brothers or sisters (for example, Davide, who said, “I was lucky because I have two older brothers and they also went to high school. So, I see the way more open”) and, finally, (4) no influence attributable to family members (for example, Fabrizio, who said, “I chose alone,” or the mother of Claudia, who said “on the choice we have not influenced any of our daughters”). The references of this coding have been presented in Table 2 .

To further understand the differences in the answers within the mother–child couple, we conducted a matrix coding query using Nvivo 12. It is a query that allows you to encode two elements (QSR international, 2021 ). The results of this query are summarized in Table 2 . The mothers involved in the study seemed to agree that no family member had any influence on the choice. In fact, all the encodings concerning the “Influences within the family” node are relative to the “None” child-node.

Livia’s mother: [My husband and I] have never said “our daughter must necessarily become a doctor or a lawyer”; the most important thing is that the profession must like her. Whatever she chooses, we do support her [...]. Anything she wants to do will be fine, the important thing is that she does it with her head, thinking well. Fabrizio’s mother: He [my son] went alone to do the career guidance interview, without telling me anything. He was curious to know what the path to being an engineer is and he inquired. I have not influenced him in this. I asked him if he wanted to be accompanied, but he preferred not to and did it all by himself. That was fine for me.

The answers given by the students are more diverse. They perpetuate the influence on the part of the mother on their choice and secondarily on the part of the father or brothers/sisters. All these influences are described as supportive, and it is interesting to note that the “father” node never appears alone but is generally associated with the “mother” node. Only four adolescents declared that they have not perceived particular influences from the family.

Davide: We talked about it as a family, with mum and dad. Interviewer: And in all this, you have managed yourself? Carlo: My mother, she was the one who looked for alternatives to the computer, also did things right on the curriculum vitae. She helped me, yes. Interviewer: And was it you pushing or were you both together? Carlo: A little bit of both. But she said to me “come on, why don’t you want to go and do this internship or see this thing?”. After I said yes, however, it was she who found the places, it was she who... yes, she helped me a lot. Interviewer: Who was helping you? Valeria: Mom and dad.

Theme 2: External influences

As regards the second alternative relating to influences, we have coded the parent-node as “external influences,” dividing it into the following child-nodes: (1) school guidance counselor, who collects the responses of the participants who referred to it at the time of the choice (for example, Fabrizio, who said, “I went to the Counselour, he gave me some help”), (2) classmates, when the influences came from classmates (for example, Davide, who said, “I saw that other classmates also chose the same thing and I felt more convinced”), (3) espoprofessioni (expoprofessions), or those who during the choice consulted the event dedicated to the professions (for example, Carlo’s mother, who said, “In September there was the event Espoprofessioni and we went”) and (4) word of mouth, when the adolescent received support from family acquaintances and friends (for example, Claudia’s mother, who said, “We had the advantage of personally meeting a doctor and we asked him for a hand”). The number of references is summarized in Table 1 . Also in this case, through the NVivo 12 software, we used a matrix coding query to further understand the differences between mothers and children in the perception of support coming from the outside. The results are shown in Table 2 . In the case of both the mothers and the students interviewed, we found that many times, during the interview, reference was made to the figure of the school guidance counselor. However, the support received was not always rated as satisfactory, in particular by three mothers and two children. Here are two examples:

Interviewer: [...] do you think the school or school guidance counselor should do something more? Livia’s mother: Yes, the counselor is not good. I must be honest. We went to the guidance office, but they put in front of the options “this, this and this” and that’s it. But even the counselor is not that he said much. He didn’t give much help. Instead, he should have asked my son what interests him, but he didn’t, for example. Interviewer: Have you seen the counselors? Claudia: I saw him, but I must say that he didn’t give me much help. My mother came once too but he didn’t help us at all.

Although to a lesser extent, some external influences come from classmates, word of mouth, or from having participated in the Espoprofessioni event, as emerged with these interviewees:

Livia’s mother: [...] We went [to the Espoprofessioni] by chance and stopped to talk to those in the health sector, medical help; there were the various schools, which illustrate their particularities. Carlo: When there were the Espoprofessioni, in that shed, we went to see and we started to decide a bit.

A final analysis we conducted was that relating to sentiment, which is used to evaluate feelings with respect to a theme. Sentiment nodes behave differently than other nodes. NVivo 12 allows you to code two parent sentiment nodes: positive and negative. Each of them has two child nodes: very and moderately. Automatic software setup aggregates child nodes to parent nodes (QSR international, 2021 ). Based on our analyses, we identified 11 positive sentiment nodes (of which 7 were positive, 2 very positive, and 2 moderately positive) and 40 negative (of which 21 were negative and 19 moderately negative). By way of example, we report some quotations of positive and negative sentiment. Parts of speech have been classified as positive sentiment:

Interviewer: [...] Are you afraid of the first day of school? Davide: No, no, in fact I can’t wait to start because I know it’s a new school. Interviewer: Are you worried about your son’s future? Fabrizio’s mother: No. Not for him. Because he finds a job for the profession he chooses anyway.

Parts of speech have been classified as negative sentiment:

Interviewer: Do you have any fears for the next few years, or do you feel calm? Federico: I’m a little afraid of what it will be like, yes; how hard it will be, yes; I have a little bit of that because I arrive from middle school, I don’t really know what the school will be like there. Federico’s mother: The context is difficult. Because it is a situation that can evolve in different ways, it is not very easy. Or the situation changes because a change is needed otherwise it is very hard. There is a lot of competition. The loss of quality in work, this need to do everything immediately, everything quickly, everything in the short term, little planned. This kind of future worries me about my son.

We were interested in verifying how these sentiments were distributed in the attributes of mother and student. The results, obtained through a matrix coding, are summarized in Table 2 . Most of the sentiment encodings have been found in the mother attribute. In general, mothers have a greater number of negative sentiments, while children have approximately equal numbers of positive and negative sentiments. This indicates that, regarding the choices and the future of the students, the mothers seem to be more concerned, while the children are also enthusiastic and curious about the new opportunities.

Data collected from the first and second questionnaires endorse the mother as the main source of support, followed by the father and other family members. These results confirm those of other studies conducted differentiating mothers and fathers as providers of social support (Colarossi & Eccles, 2003 ; Furman & Buhrmester, 1992 ; Levitt et al., 1993 ) and career-related support (Ginevra et al., 2015 ; McCabe & Barnett, 2000 ; Porfeli et al., 2013 ). Indeed, in this study, the mother is the one who most frequently participates in meetings with the school guidance counselor, regardless of her child’s gender. The mother is usually mentioned as a source of support by 91.5% of adolescents at the beginning and by 86.6% of those at the end of the last school year, although fathers are also seen as such by a good portion of the children (by 75.5% of adolescents at the beginning and 60.5% of those at the end of the last school year). The mother, followed by the father, is declared as the most important source of support. However, in line with the study of Colarossi and Eccles ( 2003 ), the data also show that daughters are more likely to perceive their mothers as sources of support and sons more likely to perceive their fathers as such, although contrary to the finding of their study in this study fathers are perceived as the second source of support, before teachers and peers. The support that adolescents, regardless of their gender, perceive as most available and also as the most important therefore comes first and foremost from their family. Gender seems to slightly differentiate the perception of support, perhaps because gender differences related to cognitive and relational styles (Eagly et al., 2004 ) lead girls to feel closer to their mother and boys to their father. Another explanation might be that the different types of professions considered by girls and boys, still largely influenced by gender stereotypes and therefore perceived as more feminine or masculine, make it more spontaneous to ask the mother or father about them. Even today, a boy is more likely to consider becoming a bricklayer than a girl, and if so, he is more likely to ask his father rather than his mother for information.

In the first part of this study, adolescents who are still making a choice and say they need more help, after parents, most often cite the school guidance counselor, while teachers are only rarely mentioned as a possible source of support. This finding seems to differ from those of other studies that highlighted the importance of support given by teachers, more than that given by parents or peers, for example, in enhancing positive career expectations in adolescents (Gushue & Whitson, 2006 ), in giving information and fostering self-efficacy in career decision-making (Cheung & Arnold, 2010 ), in fostering school identification (Kenny & Bledsoe, 2005 ), and contributing to increasing resilience, perceived employability, and self-efficacy (Di Fabio & Kenny, 2015 ). The low importance given to teachers as a source of support in relation to career choices by adolescents in this study can be explained in several ways. First of all, the “general support” that can be given by teachers in daily classes for developing important competencies that also facilitate career choices (Kivunja, 2014 ) may not be perceived as directly supporting choices by adolescents. Second, because of the way career-related support is organized in middle school in Southern Switzerland at the time of study, specific support for career decision-making is not among teachers’ main tasks. Hence, not all teachers act to facilitate the career planning of students that involves inquiring about career paths, helping them identify their interests, giving information about jobs, and helping them in setting educational and career goals (Wong et al., 2021 ). Finally, for both adolescents and their parents, in Switzerland, it is important, yes, to make a first career choice, but it is also important that this choice be crowned by successful enrollment in a school or entering into an apprenticeship contract. Hence, the most important help to achieve this last step can most easily be given by parents, as it emerges also from interviews, and, eventually, by the school guidance counselors who help the adolescent searching for practical information and, for those enrolling in VET, for an employer. Teachers for this last step can do little, aside from passing information provided by the school guidance counselor. This is perhaps also because the school guidance counselors are perceived as more important than teachers in this study, differently from other previous studies (Gysbers & Lapan, 2009 ; Multon, 2006 ).

The school guidance counselor, although emerging as a relatively important figure, is seen individually by less than half of the students over the last two years of compulsory schooling, and more than a half of them are accompanied by the mother. Although school guidance counselors are thus perceived as a source of support by a proportion of adolescents, they are definitely not the first source nor the one perceived as most useful, as highlighted in other studies (Mortimer et al., 2002 ) and also suggested by the results of the interviews conducted with this study.

In fact, the 20 interviews conducted in the second part of this study, and in particular the 10 carried out with the adolescents, confirm the importance of the mother as the first source of influence on career-related choices for them. As observed in other studies (Bernardo, 2010 ), it seems that adolescents consider it normal to be influenced by their parents in career choices. Influence from the mother is always perceived as a positive one by the child interviewed. Moreover, it is interesting to highlight that all the mothers affirm not to try to influence their child’s choice. However, referring to the support categories defined by Cutrona and Russell ( 1990 ) and by Cutrona ( 1996 ), what emerges from the interviews is that, regardless of the child’s gender, mothers provide both tangible assistance and information support (see Carlo and Claudia’s quotations), esteem (see Fabrizio or Livia’s mother’s quotations), and emotional support (see Livia’s mother or Davide’s quotations). The fact that mothers qualify this support behavior as a “non influence” on their child’s choice is an important aspect since studies have shown that this type of support is associated with greater career exploration, whereas when parents try to influence their children’s choices (Interference behavior), children experience more difficulty in career choices (Dietrich & Kracke, 2009 ; Marcionetti & Rossier, 2016a , 2016b ). The father, among the influences perceived within the family, is at second place, followed by a sister or a brother. However, the father is always cited together with the mother, further highlighting the importance of the mother for the career decision-making process of their child. As for sisters and brothers, they are an influence on choice when they are older and have already made their choice. In this case, the brother or sister, by telling and showing their experience in their chosen education, can be a role model to follow (or not to follow).

Regarding external influences, among the most frequently cited is the school guidance counselor; however, this professional figure, as has already been found in other studies (Mortimer et al., 2002 ), is not always perceived as helpful. The kind of support provided described in the interviews (see Livia’s mother quotation) seems to be purely informative, and perhaps both mothers and children expected more than information that they could probably have found on their own. The 20 interviewees, however, referred to only two school guidance counselors, so it is not the case to draw conclusions about the support provided by this professional category from these interviews. It should also be mentioned that the guidance counselors in cooperation with the Division of Vocational Training of Southern Switzerland organize every 2 years the Espoprofessioni event ( https://www4.ti.ch/decs/dfp/espoprofessioni/home/ ), which is cited as a source of useful information by two mothers and two children. Two other sources of external influence were word of mouth, i.e., the influence of family acquaintances and friends, cited by two mothers and by their children, and the classmates, cited by two adolescents. If acquaintances and friends also permit the facilitation of the organization of internships and eventually of finding an employer (i.e., they provide tangible assistance), classmates making the same choice further convince the adolescent that he/she made a good choice. According to the categorization of Cutrona and Russell ( 1990 ) and Cutrona ( 1996 ), this last type of support, more indirect, might be seen as esteem support as well as social integration/network support.

Hence, although all these figures and sources of influence play a role in the adolescent career decision-making process, this role differs both in the phase at which they intervene, in the type, and, we can assume, in its importance. In each case, mothers emerge as a kind of emotional safe haven from which children can explore themselves and professions. They encourage and accompany the child in this exploration, sometimes pointing out a possible course, which they nevertheless let the child choose whether to follow or not. At the time of the study’s conduction, other sources of support for career choice seem to be more marginal; they are tools from which to draw information or from which to get confirmation that the choice can be implemented. It is therefore not surprising that, regarding sentiments associated with the career choice, results show that though adolescents, feel both positive and negative emotions, mothers have a greater number of negative emotions. Mothers, personally involved in this important process, worry both about the choice their children have to make in the present and about their children’s future careers. Although this concern, much more typical in women/mothers than in men/fathers (e.g., Robichaud et al., 2003 ), may seem negative in some ways, it can nevertheless be the ignition engine for the career decision-making process of adolescents (Young et al., 1997 ), who are not always ready to initiate it spontaneously.

Limitations and future directions

This study allows more light to be shed on perceptions related to sources of support and influence in adolescents’ career choices, also taking into account both the gender of the adolescent and the distinction between mother and father. First, using a quantitative approach, it allowed them to be put in order of importance, and highlighting some differences in their perception related to the adolescent’s gender. Second, with a quali-quantitative analysis approach, it permitted the highlighting of the sources of influence and support in career choice perceived by 10 mother–child dyads and to highlight some differences in perception and emotionality between the two figures.

However, there are two limitations of this study to take into account. The first concerns the fact that, in the first part of the study, the adolescents’ perceived sources of support were taken into account, but without going into the type of support provided or the actual effectiveness of it. Also, the limited number of participants when divided into the various subgroups of males and females and those who saw the school guidance counselor did not allow for statistical tests to be conducted to assess reliably differences in perceptions between males and females. Moreover, only 10 mother–child dyads referring to two middle schools were interviewed. Extending the number of interviewees referring to a bigger number of middle schools, and thus, of school guidance counselors, might have permitted further investigation of the perceived effectiveness of this figure in supporting career choices. Moreover, a bigger number of interviews, also involving fathers (hence, also involving father–children dyads or father–mother–children triads), could have permitted a deeper investigation into the eventual differentiating discourses in relation to the influence and type of support provided by the mother and by the father. Finally, although the results are encouraging in indicating the presence of family support for career choice, it would be interesting to study the causes and effects of too invasive support (interference) or even of a lack of support (lack of engagement), considering the effect of gender, both of the adolescent who suffers it and of the parent who enacts it.

Knowing what the main sources of support for career choice perceived by adolescents are is important, and despite its limitations, this study permits the shedding of some more light on this subject. The fact that it is primarily the mother who supports her son or daughter indicates, for example, that specific interventions aimed at developing competencies for supporting choices in external sources of support should be directed primarily to this figure. The fact that daughters perceive (and expect?) even more support from their mothers than boys may also indicate that, where this support is lacking, they are even more likely to struggle than boys, who also more often consider fathers as a source of support. As already highlighted by other studies, it would be important to put more emphasis on the role of the guidance counselor, who, although perceived as a possible source of support, is still an underutilized figure and not always judged effective in providing help in the school setting. Unlike a guidance counselor who provides their services outside of school, this figure in school is limited in the time they have to follow up with students, and this may also affect the effectiveness of their intervention. An alternative could be to increase the amount of time this figure is in school or the type of support/intervention provided. Instead of face-to-face interviews, the literature seems to indicate that group interventions aimed at developing specific knowledge and competencies useful in career decision-making might be most effective in helping young people make choices and implement them (e.g., Mahat et al., 2022 ; Nota et al., 2016 ; Zammitti et al., 2020 ). Teachers might be involved, together with school guidance counselors, in providing these interventions. Indeed, as the figure who, after parents, spends the most time with adolescents and best knows each one of them, the teacher should be more involved in supporting students’ choices, especially given the positive outcomes of studies in which this figure is trained to make available this kind of support (e.g., Wong et al., 2021 ). Teacher support should be career-specific (Wong et al., 2021 ) but also, and perhaps mainly, a general informational, instrumental, and emotional support aiming at the development of competencies useful also for career decision-making process, such as self-exploration and awareness, decision-making competencies, relational competencies (for asking and providing support), autonomy, and self-determination (Kivunja, 2014 ; Lei et al., 2018 ).

Alm, S., Laftman, S. B., Sandahl, J., & Modin, B. (2019). School effectiveness and students’ future orientation: A multilevel analysis of upper secondary students in Stockholm Sweden. Journal of Adolescence, 70 , 62–73. https://doi.org/10.1016/j.adolescence.2018.11.007

Article   Google Scholar  

Bernardo, A. B. (2010). Exploring Filipino adolescents’ perceptions of the legitimacy of parental authority over academic behaviors. Journal of Applied Developmental Psychology, 31 (4), 273–280. https://doi.org/10.1016/j.appdev.2010.03.003

Blustein, D. L., Prezioso, M. A., & Schultheiss, D. P. (1995). Attachment theory and career development: Current status and future directions. Counseling Psychologist, 23 (3), 416–432. https://doi.org/10.1177/0011000095233002

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology: Research designs: Quantitative, qualitative, neuropsychological, and biological (Vol. 2, pp. 57–71). American Psychological Association.

Chapter   Google Scholar  

Cairo, P. C., Kritis, K. J., & Myers, R. M. (1996). Career assessment and the adult career concerns inventory. Journal of Career Assessment, 4 (2), 189–204. https://doi.org/10.1177/106907279600400205

Cheung, R., & Arnold, J. (2010). Antecedents of career exploration among Hong Kong Chinese university students: Testing contextual and developmental variables. Journal of Vocational Behavior, 76 (1), 25–36. https://doi.org/10.1016/j.jvb.2009.05.006

Code, M., & Bernes, K. (2006). Adolescents’ perceptions of career concerns. The National Consultation on Career Development (NATCON). Retrieved from http://www.natcon.org/natcon/papers/natconpapers2006e9.pdf

Colarossi, L. G. (2001). Adolescent gender differences in social support: Structure, function, and provider type. Social Work Research, 25 (4), 233–241. https://doi.org/10.1093/swr/25.4.233

Colarossi, L. G., & Eccles, J. S. (2003). Differential effects of support providers on adolescents’ mental health. Social Work Research, 27 (1), 19–30. https://doi.org/10.1093/swr/27.1.19

Creed, P. A., Fallon, T., & Hood, M. (2009). The relationship between career adaptability, person and situation variables, and career concerns in young adults. Journal of Vocational Behavior, 74 (2), 219–229. https://doi.org/10.1016/j.jvb.2008.12.004

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches . Sage publications.

Google Scholar  

Cutrona, C. E. (1996). Social support in couples: Marriage as a resource in times of stress . Sage.

Book   Google Scholar  

Cutrona, C. E., & Russell, D. W. (1990). Type of social support and specific stress: toward a theory of optimal matching. In B. R. Sarason, I. G. Sarason, & G. R. Pierce (Eds.), Social support: An interactional view (pp. 319–366). Wiley.

Dietrich, J., & Kracke, B. (2009). Career-specific parental behaviors in adolescents’ development. Journal of Vocational Behavior, 75 (2), 109–119. https://doi.org/10.1016/j.jvb.2009.03.005

Di Fabio, A., & Kenny, M. E. (2015). The contributions of emotional intelligence and social support for adaptive career progress among Italian youth. Journal of Career Development, 42 (1), 48–59. https://doi.org/10.1177/0894845314533420

Eagly, A. H., Beall, A., & Sternberg, R. S. (Eds.). (2004). The psychology of gender (2nd ed.). Guilford Press.

Fouad, N. A., Kantamneni, N., Smothers, M. K., Chen, Y.-L., Fitzpatrick, M., & Terry, S. (2008). Asian American career development: A qualitative analysis. Journal of Vocational Behavior, 72 (1), 43–59. https://doi.org/10.1016/j.jvb.2007.10.002

Furman, W., & Buhrmester, D. (1992). Age and sex differences in perceptions of networks of personal relationships. Child Development, 63 (1), 103–115. https://doi.org/10.2307/1130905

Garcia, P. R. J. M., Restubog, S. R. D., Toledano, L. S., Tolentino, L. R., & Rafferti, A. L. (2012). Differential moderating effects of student- and parent-rated support in the relationship between learning goal orientation and career decision-making self-efficacy. Journal of Career Assessment, 20 (1), 22–33. https://doi.org/10.1177/1069072711417162

Gati, I., Gadassi, R., Saka, N., Hadadi, Y., Ansenberg, N., Friedmann, R., et al. (2011). Emotional and personality-related aspects of career decision-making difficulties: facets of career indecisiveness. Journal of Career Assessment, 19 (1), 3–20. https://doi.org/10.1177/1069072710382525

Gati, I., & Saka, N. (2001). High school student’s career-related decision-making difficulties. Journal of Counselling Development, 79 (3), 331–340. https://doi.org/10.1002/j.1556-6676.2001.tb01978.x

Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Methods of data collection in qualitative research: Interviews and focus groups. British Dental Journal, 204 (6), 291–295. https://doi.org/10.1038/bdj.2008.192

Ginevra, M. C., Nota, L., & Ferrari, L. (2015). Parental support in adolescents’ career development: Parents’ and children’s perceptions. Career Development Quarterly, 63 (1), 2–15. https://doi.org/10.1002/j.2161-0045.2015.00091.x

Guerra, A. L., & Braungart-Rieker, J. M. (1999). Predicting career indecision in college students: The roles of identity formation and parental relationship factors. Career Development Quarterly, 47 (3), 255–266. https://doi.org/10.1002/j.2161-0045.1999.tb00735.x

Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Validity and reliability (credibility and dependability) in qualitative research and data analysis. Applied Thematic Analysis, 79 , 106.

Gushue, G. V., & Whitson, M. L. (2006). The relationship among support, ethnic identity, career decision self-efficacy, and outcome expectations in African American high school students applying social cognitive career theory. Journal of Career Development, 33 (2), 112–124. https://doi.org/10.1177/0894845306293416

Gysbers, N. C., & Lapan, R. T. (2009). Strengths-based career development for school guidance and counseling programs . Counseling Outfitters.

Hardon, A., Hodgkin, C., & Fresle, D. (2004). How to investigate the use of medicines by consumers . World Health Organization and University of Amsterdam. Retrieved October 18, 2021, from http://apps.who.int/medicinedocs/en/d/Js6169e/on

Hirschi, A., Niles, S. G., & Akos, P. (2011). Engagement in adolescent career preparation: Social support, personality and the development of choice decidedness and congruence. Journal of Adolescence, 34 (1), 173–182. https://doi.org/10.1016/j.adolescence.2009.12.009

Ishii, K., Mojaverian, T., Masuno, K., & Kim, H. S. (2017). Cultural differences in motivation for seeking social support and the emotional consequences of receiving support: The role of influence and adjustment goals. Journal of Cross-Cultural Psychology, 48 (9), 1442–1456. https://doi.org/10.1177/0022022117731091

Javadi, M., & Zarea, K. (2016). Understanding thematic analysis and its pitfall. Journal of Client Care, 1 (1), 33–39. https://doi.org/10.15412/J.JCC.02010107

Kelly, S. E. (2010). Qualitative interviewing techniques and styles. In I. Bourgeauly, R. Dingwall, & R. De Vries (Eds.), SAGE handbook of qualitative methods in health research (pp. 307–327). SAGE.

Kenny, M. E., & Bledsoe, M. (2005). Contributions of the relational context to career adaptability among urban adolescents. Journal of Vocational Behavior, 66 (2), 257–272. https://doi.org/10.1016/j.jvb.2004.10.002

Kenny, M., & Medvide, M. B. (2013). Relational influences on career development. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 329–356). Wiley.

Kivunja, C. (2014). Do you want your students to be job-ready with 21st Century Skills? Change pedagogies: A paradigm shift from vygotskyian social constructivism to critical thinking, problem solving and Siemens’ digital connectivism. International Journal of Higher Education, 3 (3), 81–91. https://doi.org/10.5430/ijhe.v3n3p81

Kobak, R., Ferenz-Gillies, R., Everhart, E., & Seabrook, L. (1994). Maternal attachment strategies and emotional regulation with adolescent offspring. Journal of Research on Adolescence, 4 (4), 553–566. https://doi.org/10.1207/s15327795jra0404_7

Kracke, B. (1997). Parental behaviors and adolescents’ career exploration. Career Development Quarterly, 45 (4), 341–350. https://doi.org/10.1002/j.2161-0045.1997.tb00538.x

Krippendorff, K. (1980). Content analysis: An introduction to its methodology . Sage Publications.

Leaper, C., Anderson, K. J., & Sanders, P. (1998). Moderators of gender effects on parents’ talk to their children: A meta-analysis. Developmental Psychology, 34 (1), 3–27. https://doi.org/10.1037/0012-1649.34.1.3

Lei, H., Cui, Y., & Chiu, M. M. (2018). The relationship between teacher support and students’ academic emotions: A meta-analysis. Frontiers in Psychology, 8 , 2288. https://doi.org/10.3389/fpsyg.2017.02288

Lent, R. W., Brown, S. D., Nota, L., & Soresi, S. (2003). Testing social cognitive interest and choice hypotheses across Holland types in Italian high school students. Journal of Vocational Behavior, 62 (1), 101–118. https://doi.org/10.1016/S0001-8791(02)00057-X

Leung, S. A., Hou, Z.-J., Gati, I., & Li, X. (2011). Effects of parental expectations and cultural-values orientation on career decision-making difficulties of Chinese university students. Journal of Vocational Behavior, 78 (1), 11–20. https://doi.org/10.1016/j.jvb.2010.08.004

Levitt, M. J., Guacci-Franco, N., & Levitt, J. L. (1993). Convoys of social support in childhood and early adolescence: Structure and function. Developmental Psychology, 29 (5), 811–818. https://doi.org/10.1037/0012-1649.29.5.811

Lodi, E., Di Chiacchio, C., Santilli, M., D’Amario, B., & Avella, S. (2008). Aree problematiche degli adolescenti e strategie di coping. Analisi del contenuto di alcuni focus group [Adolescents’ problem areas and coping strategies. Content analysis of some focus groups]. In F. Petruccelli, V. Verrastro, & B. D’Amario, Dalla scuola all’università: una scelta di vita [From school to college: a life choice] (pp. 267–274). Franco Angeli.

Ma, P.-W.W., & Yeh, C. J. (2010). Individual and familial factors influencing the educational and career plans of Chinese immigrant youths. Career Development Quarterly, 58 (3), 230–245. https://doi.org/10.1002/j.2161-0045.2010.tb00189.x

Mahat, M., Dollinger, M., D’Angelo, B., Naylor, R., & Harvey, A. (2022). Co-designing a curriculum model for career education: Perspectives from regional communities in Australia. Australian Educational Researcher . https://doi.org/10.1007/s13384-021-00505-0

Malina, M. A., Nørreklit, H. S., & Selto, F. H. (2011). Lessons learned: Advantages and disadvantages of mixed method research. Qualitative Research in Accounting & Management, 8 (1), 59–71.

Marcionetti, J., & Rossier, J. (2016a). The parental career-related behaviors (PCB) questionnaire: Italian validation. Testing, Psychometrics, Methodology in Applied Psychology, 23 , 347–363. https://doi.org/10.4473/TPM23.3.6

Marcionetti, J., & Rossier, J. (2016b). The mediating impact of parental support on the relationship between personality and career indecision in adolescents. Journal of Career Assessment, 25 (4), 601–615. https://doi.org/10.1177/1069072716652890

Mayring, P. (2000). Qualitative content analysis [28 paragraphs]. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 1 (2). http://nbn-resolving.de/urn:nbn:de:0114-fqs0002204

McCabe, K. M., & Barnett, D. (2000). The relation between familial factors and the future orientation of urban, African American sixth graders. Journal of Child & Family Studies, 9 (4), 491–508. https://doi.org/10.1023/A:1009474926880

Mortimer, J. T., Zimmer-Gembeck, M. J., Holmes, M., & Shanahan, M. J. (2002). The process of occupational decision-making. Journal of Vocational Behavior, 61 (3), 439–465. https://doi.org/10.1006/jvbe.2002.1885

Multon, K. D. (2006). Career development. In N. J. Salkind (Ed.), Encyclopedia of human development (pp. 219–226). Sage.

Nota, L., Santilli, S., & Soresi, S. (2016). A life-design-based online career intervention for early adolescents: Description and initial analysis. Career Development Quarterly, 64 (1), 4–19. https://doi.org/10.1002/cdq.12037

Nota, L., & Soresi, S. (2002). Il career problem solving. In L. Nota, L. Mann, S. Soresi, & I. A. Friedman (a cura di), Scelte e decisioni scolastico-professionali [School-professional choices and decisions] (pp. 67–94). ITER.

Parola, A., & Marcionetti, J. (2020). Career orientation: A qualitative study of the best practices in the Swiss context. Mediterranean Journal of Clinical Psychology . https://doi.org/10.6092/2282-1619/mjcp-2552

Parola, A., & Marcionetti, J. (2021). Career indecision and life satisfaction: The role of career-related parental behaviors and career adaptability. Journal of Career Development . https://doi.org/10.1177/0894845321995571

Polit, D. S., & Beck, C. T. (2010). Essentials of nursing research. Appraising evidence for nursing practice (7th ed.). Lippincott-Raven Publishers.

Porfeli, E., Ferrari, L., & Nota, L. (2013). Work valence as a predictor of academic achievement in the family context. Journal of Career Development, 40 (1), 371–389. https://doi.org/10.1177/0894845312460579

QSR international. (2021). NVivo 12 . Retrieved from https://help-nv.qsrinternational.com/12/win/v12.1.108-d3ea61/Content/welcome.htm#

Robichaud, M., Dugas, M. J., & Conway, M. (2003). Gender differences in worry and associated cognitive-behavioral variables. Journal of Anxiety Disorders, 17 (5), 501–516. https://doi.org/10.1016/s0887-6185(02)00237-2

Rogers, M. E., & Creed, P. A. (2011). A longitudinal examination of adolescent career planning and exploration using a social cognitive career theory framework. Journal of Adolescence, 34 (1), 163–172. https://doi.org/10.1016/j.adolescence.2009.12.010

Rubin, H. J., & Rubin, I. S. (2005). Qualitative interviewing: The art of hearing the data (2nd ed.). SAGE.

Savickas, M. L., Passen, A. J., & Jarjoura, D. G. (1988). Career concern and coping as indicators of adult vocational development. Journal of Vocational Behavior, 33 (1), 82–98. https://doi.org/10.1016/0001-8791(88)90035-8

Schultheiss, D., Kress, H., Manzi, A., & Glasscock, J. (2001). Relational influences in career development: A qualitative inquiry. Counseling Psychologist, 29 (2), 216–241. https://doi.org/10.1177/00110000012920

Soresi, S., & Nota, L. (2015). Il couselling del futuro [Couselling of the future]. Cleup.

Stick, S. L., & Lincoln, N. (2006). Using mixed-methods sequential explanatory design. From Theory to Practice, 18 (1), 3–20.

Taylor, M. C. (2005). Interviewing. In I. Holloway (Ed.), Qualitative research in health care (pp. 39–55). McGraw-Hill Education.

Tomasetto, C., & Selleri, P. (2004). Lessico dell’intervista, lessico degli intervistati: L’articolazione tra domande e risposte nell’analisi di alceste [Interview vocabulary, interviewees’ vocabulary: The articulation between questions and answers in the analysis of alceste]. Journées Internationales D’analyse Statistique Des Données Textuelles, 7 , 1051–1060.

Wang, L.-F., & Heppner, P. P. (2002). Assessing the impact of parental expectations and psychological distress on Taiwanese college students. Counseling Psychologist, 30 (4), 582–608. https://doi.org/10.1177/00100002030004006

Wengraf, T. (2001). Qualitative research interviewing: Biographic narrative and semi-structured methods . SAGE.

Whiston, S., & Keller, B. (2004). The influences of the family of origin on career development: A review and analysis. Counseling Psychologist, 32 (4), 493–568. https://doi.org/10.1177/0011000004265660

Wong, L. P. W., Yuen, M., & Chen, G. (2021). Career-related teacher support: A review of roles that teachers play in supporting students’ career planning. Journal of Psychologists and Counsellors in Schools, 31 (1), 130–141. https://doi.org/10.1017/jgc.2020.30

Young, R. A., & Friesen, J. D. (1992). The intentions of parents in influencing the career development of their children. Career Development Quarterly, 40 (3), 198–207. https://doi.org/10.1002/j.2161-0045.1992.tb00326.x

Young, R. A., Paseluikho, M. A., & Valach, L. (1997). The role of emotion in the construction of career in parent—adolescent conversations. Journal of Counseling & Development, 76 (1), 36–44. https://doi.org/10.1002/j.1556-6676.1997.tb02374.x

Zammitti, A., Magnano, P., & Santisi, G. (2020). “Work and Surroundings”: A training to enhance career curiosity, self-efficacy, and the perception of work and decent work in adolescents. Sustainability, 12 (16), 6473. https://doi.org/10.3390/su12166473

Zhao, X., Lim, V. K. G., & Teo, T. S. H. (2012). The long arm of job insecurity: Its impact on career-specific parenting behaviors and youths’ career self-efficacy. Journal of Vocational Behavior, 80 (3), 619–628. https://doi.org/10.1016/j.jvb.2012.01.018

Download references

Open access funding provided by SUPSI - University of Applied Sciences and Arts of Southern Switzerland. This research was partially funded by the Department of Education and Learning (DFA) of the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), but no funding was received to assist with the preparation of this manuscript.

Author information

Authors and affiliations.

Department of Education and Learning (DFA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Piazza San Francesco 19, 6600, Locarno, Switzerland

Jenny Marcionetti

Department of Educational Sciences, University of Catania, Catania, Italy

Andrea Zammitti

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jenny Marcionetti .

Ethics declarations

Conflict of interest.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval

The study complies with Swiss Psychological Society ethical standards.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Marcionetti, J., Zammitti, A. Perceived support and influences in adolescents’ career choices: a mixed-methods study. Int J Educ Vocat Guidance (2023). https://doi.org/10.1007/s10775-023-09624-9

Download citation

Received : 23 January 2023

Accepted : 16 August 2023

Published : 02 September 2023

DOI : https://doi.org/10.1007/s10775-023-09624-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Career choice
• Adolescents
• Find a journal
• Publish with us
• Track your research

PERSPECTIVE article

The art of influencing consumer choices: a reflection on recent advances in decision neuroscience.

• 1 School of Psychology, University of Plymouth, Plymouth, United Kingdom
• 2 Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
• 3 Department of Psychology and Cognitive Science, University of Trento, Trento, Italy

In recent years, our knowledge concerning the neurobiology of choice has increased tremendously. Research in the field of decision-making has identified important brain mechanisms by which a representation of the subjective value of an option is built based on previous experience, retrieved and compared to that of other available options in order to make a choice. One body of research, in particular, has focused on simple value-based choices (e.g., choices between two types of fruits) to study situations very similar to our daily life decisions as consumers. The use of neuroimaging techniques has deepened and refined our knowledge of decision processes. Additionally, computational approaches have helped identifying and describing the mechanisms underlying newly found components of the decisional process. They provide mechanistic explanations for diverse biases that can drive decision makers away from their own preferences or from rational choices. It is now clear that both attentional and affective factors can exert robust effects on an individual’s decisions. Because these factors can be manipulated externally, academic research and theories are of great interest to the marketing industry. This approach is becoming increasingly effective in manipulating consumer behavior and has the potential to become even more effective in the future. Another line of research has revealed differences in the decision-making neural circuitry that underlie sub-optimal choice behavior, rendering some individuals particularly vulnerable to marketing strategies. As neuroscientists, we wonder whether relevant institutions should direct their efforts toward raising citizens’ awareness, demanding more transparency on marketing applications and regulate the most pervasive communication techniques in marketing, in view of their current use and of recent research progress.

Attentional Biases in Consumer Choices

Tremendous progress has been realized in the last decade in our understanding of attentional effects on decision processes, through the description of their neurocomputational mechanisms. Thus, we will focus here on those mechanisms to illustrate how they can inform marketing strategies. Psychological and neural accounts of the role of memory and affective mechanisms in consumer decisions can be found in Plassmann and Karmarkar (2015) .

Cognitive and Neural Mechanisms of Simple Choice

When facing a simple decision, for instance picking a fruit to eat in a basket containing several types of fruits, our brain computes a value signal. The value represents the expected benefit of consuming the good based on previous experience. Recent cognitive models of decision-making propose that a value is assigned to all the options available, then the values are compared in order to reach a decision ( Rangel and Clithero, 2014 ). Expected delays, potential price, or uncertainty in its obtainment of the good will all be incorporated into the value signal. How exactly the value is computed, though, is still under scrutiny. Much evidence supports the theory that values are computed through reinforcement learning. A value is updated when our experience in consuming the good does not match our expectations, a mechanism that supports adaptive behavior. This learning mechanism is implemented in the brain by dopaminergic neurons of the ventral striatum. These neurons encode a prediction error signal which serves as an update signal for the value ( Schultz, 1998 ; Tobler et al., 2005 ). They project to a frontal region called the ventromedial prefrontal cortex, which is thought to store the value signal ( Ruff and Fehr, 2014 ). However, the way we value options often depends on our internal states (e.g., how hungry we are at that particular moment) and on states of the world (we might value more a juicy fruit in the summer than in the winter). Assuming that values of goods are stored globally fails to explain why choices can vary with the decision context.

Another theory proposes that we separately evaluate all attributes of the available options and integrate them at the time of choice ( Rangel and Clithero, 2014 ). The value of an apple is not represented as such; rather, value associated with its color, taste, smell or shape are encoded separately. Considering the attributes of a good and retrieving the values associated with those attributes requires attention.

The Influence of Attention on Decisions

Krajbich and Rangel (2011) proposed that attention fluctuates among the different items being evaluated during a decision, and this affects the computation of their value. They applied a well-established model in perceptual decision-making ( Ratcliff, 1978 ; Ratcliff et al., 2016 ) to simple value-based choices in order to characterize the link between attention – as measured by eye gaze and decision latency – to decision output through the hidden value computation process. Their attentional drift diffusion model (aDDM), applied to binary choices, states that the values of the attributes of the currently attended item are retrieved and integrated ( Krajbich et al., 2010 ; but see Summerfield and Tsetsos, 2012 ; Calluso et al., 2015 ; for alternative drift diffusion models of value-based decision). At any point of time, the integrated value is then compared to the value of the unattended item. The agent freely explores the available options, switching their attention among the items. If the two items are appetitive (i.e., have been associated with positive experience in the past), the retrieval of their value will yield to a positive signal. While a specific item is being fixated, its value is computed and its relative value, compared to the other item, increases. When the difference between the values of the two items reaches a given threshold, the decision process terminates ( Krajbich et al., 2010 ).

Evidence supporting this model is provided by experiments in perceptual decision-making (e.g., is the left segment shorter than the right one?) showing that in every choice, the firing rate of neurons increases proportionally to the easiness of the decision (integration process) and reaches the same point (threshold) right before an answer is given ( Roitman and Shadlen, 2002 ; Gold and Shadlen, 2007 ). Moreover, during binary choices between snacks, the striatum and the ventromedial prefrontal cortex (i.e., two brain areas involved in valuation and choices) encode the value of the attended item, relatively to the value of the unattended item ( Hare et al., 2011 ; Lim et al., 2011 ). Thus, attention modulates brain activity related to the retrieval and comparison of values.

The theory has several implications which have been verified experimentally. First, because the value of a desirable item increases when it is attended, the chosen item is the last one to be fixated before the threshold is reached and the decision is made. Second, the first fixated item gets an advantage in the value computation process and thus is more likely to be chosen. Third, the longer an item is being looked at the more likely it is that it will be chosen. Using repeated choices between snacks in combination with eye tracking, Krajbich et al. (2010) were able to confirm all those predictions. When choosing between two snacks equally liked by participants, they picked the last fixated item in about 75% of the trials. Moreover, the longest the first fixation, the higher the probability that the corresponding item would be chosen. Lastly, the longest an item was fixated and the higher was the probability it would be chosen, even after correcting for liking ratings. Importantly, similar choice biases induced by fixation trajectories were observed during purchasing decisions ( Krajbich et al., 2012 ).

Manipulating Attention to Bias Consumer Choices

As decision processes are strongly influenced by visual exploration, this evidence may imply that externally orienting attention would result in systematic decision biases. Indeed, controlling the duration of visual presentation of the options can change judgments about the attractiveness of human faces ( Shimojo et al., 2003 ) and about moral situations ( Pärnamets et al., 2015 ). Decisions to acquire food or art items ( Armel et al., 2008 ; Lim et al., 2011 ) 1 can be biased as well. The likelihood that an item is chosen increases between 6 and 11% when it was seen for 900 ms rather than 300 ms. Therefore, people have a bias to choose the things they have been viewing the longest rather than those they genuinely prefer. Gaze patterns reflect the preferences of individuals; they influence those preferences as well.

In addition, visually salient items would grab more attention ( Itti and Koch, 2001 ), hence be fixated first and longer, and ultimately be chosen more often. Studies have shown that manipulating the visual saliency of stimuli by varying features such as intensity, color, and orientation results in participants making a choice that contradicts their initial preferences ( Navalpakkam et al., 2010 ; Towal et al., 2013 ). These effects extend to purchasing environments, where they can become even stronger when the cognitive load is high. The color, and brightness of the packaging can lead individuals to choose their least preferred product under time pressure ( Milosavljevic et al., 2012 ). Similarly, the probability that individuals will pick the brand they value the most in a supermarket shelf decreases as the number of available products increases. They tend to grab the product right in front of them. Because of reading habits, in occidental countries, options placed in the top left corner are chosen more often than those in lower right corner ( Reutskaja et al., 2011 ).

Applications in Marketing

Clearly, advertisers did not wait for psychologists and neuroscientists to describe the cognitive mechanisms of the attention grabbing effects on decisions to exploit them ( Pieters and Wedel, 2004 ). Nonetheless as academic research makes progress in identifying decision biases, precisely describing the variables that can cause these biases in more and more refined theoretical models, advertising and other marketing techniques will become more effective. In fact, many efforts are directed into bridging neuroscience research with marketing both at the academic and at the industry levels ( Plassmann et al., 2007 ; Karmarkar and Plassmann, 2019 ). Marketing companies are now equipped with a more mechanistic understanding of decisions processes and various neuroscientific tools to measure affective responses (skin conductance responses, pupil dilatation), attentional effects (eye movements, mouse movements), and brain responses elicited by products.

One particularly problematic ethical concern that derives from those new approaches is the ability to target specific individuals or groups of individuals ( Stanton et al., 2017 ) via the systematic monitoring of consumers’ behavior, both online and in shops and the use of big data techniques to profile them ( Aguirre et al., 2015 ; Boerman et al., 2017 ). The goal is to identify the putative needs of categories of consumers in order to focus the marketing strategy on selected goods susceptible to fill those needs. There are several risks associated with this practice, one being an increased consumerism and increased prices paid by consumers ( Stanton et al., 2017 ). Another risk is to exploit the vulnerabilities of individuals. For instance, individuals, with compulsive buying disorders ( Black, 2007 ) are particularly sensitive to encouragements to buy on the web ( Rose and Dhandayudham, 2014 ). Marketing techniques can potentially have detrimental consequences on several groups of the population.

Inter-Individual Differences in Decision-Making and Vulnerability to Marketing

Large inter-individual differences exist, both in decision mechanisms and their susceptibility to external influence. During development and aging, individuals tend to make less advantageous choices and are more susceptible to the influence of marketing techniques. Addiction and eating disorders can deeply tamper with the ability of making healthy choices. Recent advances in cognitive psychology and neuroscience can help understand why many individuals struggle in making sound choices.

Children and Adolescents

Compared to adults, adolescents engage more in risky behavior ( Steinberg, 2008 ) and display heightened peer-influence in their daily choices ( van Hoorn et al., 2016 ). The uneven neurodevelopmental trajectories of the brain systems implicated in processing rewards on one side, and those involved in cognitive control on the other can explain these behavioral characteristics ( Casey et al., 2008 ). The hyper-reactivity of the reward system, especially in the striatum is associated with emotional hypersensitivity to rewarding stimuli, faces and socio-emotional stimuli ( Galvan et al., 2006 ; Casey et al., 2008 ; Hare et al., 2008 ). By contrast, the maturation of the prefrontal cortex, involved in cognitive control, still continues until about the age of 20 ( Gogtay et al., 2004 ; Shaw et al., 2008 ).

Younger consumers constitute a substantial part of the market and marketers and advertisers have developed a large spectrum of strategies to reach them ( Valkenburg and Cantor, 2001 ). The interest for marketing in children and adolescents lays in the realization that, in the last decades, they have acquired higher financial independence and more influence in household purchasing decisions. Children develop brand loyalty at an early age ( Haryanto et al., 2016 ), which persists until adulthood. Detrimental effects of advertising on the development of children’s consumption habits is well documented ( Wilcox et al., 2004 ). Television commercials targeted at children, in particular, are highly effective ( Atkin, 1978 ; Gorn and Goldberg, 1982 ). They have been reported to induce unhealthy eating habits, to cultivate a materialistic value system and to be a source of conflicts between children and their parents ( Goldberg and Gorn, 1978 ; Gorn and Goldberg, 1982 ; Story and French, 2004 ).

Older Adults

Aging individuals constitute a particularly vulnerable population as well. Older individuals make more disadvantageous decisions, especially in uncertain or changing environments. One exception is the ability to make more farsighted decisions with age ( Samanez-Larkin and Knutson, 2015 ) which can potentially lead to better consumer choices ( Zauberman and Urminsky, 2016 ). However, older adults borrow at higher interest rates and pay more fees to financial institutions than their younger counterparts ( Agarwal et al., 2007 ); they are less consistent in health-related decisions ( Löckenhoff and Carstensen, 2007 ). Most importantly they are more sensitive to deceptive advertising than their younger counterparts ( Denburg et al., 2007 ). Older adults’ heightened susceptibility to misleading advertising techniques can be explained by a reduced ability to discriminate between potentially misleading and more truthful advertising claims ( Gaeth and Heath, 1987 ). They tend as well to give higher credit to claims that are repeated. Strikingly, even if they are informed that a claim is false, they will remember it as true a few days later ( Skurnik et al., 2005 ). Decision deficits that arise with age in variable or uncertain environments might be due to cognitive limitations ( Henninger et al., 2010 ; van de Vijver et al., 2015 ). Deficits in valuation processes have been also reported at the neural level, as structural changes in frontostriatal pathways are linked to disadvantageous decisions ( Samanez-Larkin and Knutson, 2015 ; van de Vijver et al., 2016 ).

Inter-Individual Differences in Self-Control

Individuals differ widely in their ability to implement self-control in their daily choices and maintain goal-directed behavior. Economists explain these disparities by considering inter-individual differences in discounting the long term consequences of choice options in the computation of their value ( Laibson, 1997 ; O’Donoghue and Rabin, 1999 ). Psychologists approach this question by considering the relative difficulty and reliability of representing immediate pleasurable attributes and more abstract and temporally distant attributes of options ( Liberman and Trope, 2008 ). When applied to self-control in dietary choices, eating a chocolate cake rather than an apple can be explained by the overweighing of taste compared to health information. A computational approach showed that up to 39% of the variability in dietary self-control failures can be explained by the speed with which the decision-making circuitry processes basic attributes like taste, versus more abstract attributes such as health ( Sullivan et al., 2015 ). The biological plausibility of this model was supported by the finding that variability in diet success is linked to the relative representation of taste and health attributes in the ventromedial prefrontal cortex ( Hare et al., 2009 ). According to the authors, “these findings provide a rationale for regulating marketing practices that increase the relative ease with which abstract attributes such as health are processed. For example, prominently displaying health information such as calorie counts may allow more rapid integration of health attributes” ( Sullivan et al., 2015 , p. 133).

In sum, the brain structures involved in motivation and decision-making are the latest to be fully functional during development and decline relatively early with age ( Somerville and Casey, 2010 ; Samanez-Larkin and Knutson, 2015 ). As a result, maintaining goal-directed behavior in the long term and resisting temptations can be difficult at young age. Later in life, flexibly adapting to changing decision environments can become challenging ( Eppinger et al., 2011 ). During adult life, unhealthy habits can readily form and several biological or societal factors can dysregulate the balance of the decision-making and motivation brain circuitry. Thus, large portion of the population is susceptible to be negatively impacted by marketing techniques and make disadvantageous decision or forming unhealthy habits, at least during certain period of their lives.

Advertising Regulation

The realization of the increasing potential of neuroscientific knowledge applied to marketing raises a few questions. Does this always represent an advantage to us as a society and as individuals? If not, should (more) regulations be put in place to avert potential damage?

Why Regulate Advertising?

In a world full of temptations carried by pervasive marketing messages, making decisions consistent with one’s own goals and preferences requires constant self-control. Extensive research has revealed that self-control often fails when individuals experience emotional distress ( Baumeister et al., 1994 ). Excessive exposure to social norms brought by advertisement can induce emotional distress in vulnerable populations such as addicts or individuals with eating disorders. For instance, exposure to thin models in advertisement induces body-focused anxiety among women ( Halliwell and Dittmar, 2004 ).

Research on the psychological consequences of poverty indicates a link between low income, stress and short-sighted, disadvantageous economical decisions ( Haushofer and Fehr, 2014 ). In addition, financial scarcity causes a reduction in cognitive control ( Mani et al., 2013 ), as well as changes in attention allocation; salient information relative to short-term decisions receive more attention than information concerning the future, which can cause bad economic decisions such as over-borrowing ( Shah et al., 2012 ). Consequently, we might reasonably expect that poorer individuals can be negatively affected by advertising. While positive nudging can elicit people to save more ( Karlan et al., 2016 ), tempting advertising or branding effects can easily lead to over-spending. Whether overexposure to marketing messages is linked to decreased well-being and increased level of stress or emotional distress in the general population is unknown, although some authors suggest it is likely to be the case ( Baumeister, 2002 ; Sullivan et al., 2015 ). Research investigating this question is crucially needed in order to have a sound scientific dialogue about the “dark side of consumer neuroscience” ( Kenning and Plassmann, 2008 ).

Internet advertising, in particular, potentially constitutes a serious concern. Internet ads are present in the visual field of consumers even when not directly attended. Several studies have shown that the value associated with specific stimuli are retrieved and updated by our reward system even when passively viewed ( Lebreton et al., 2009 ; Tusche et al., 2010 ; Smith et al., 2014 ). Passive viewing of products of a specific brand have direct effect on purchase decisions ( Ferraro et al., 2009 ). Additionally, with the generalization of online shopping, ads are present in the visual field of the buyer right at the moment of purchasing decisions. The use of internet data enables the tailoring of adverts by proposing to specific consumers those products they would be more likely to purchase. Online targeted advertising, through the monitoring of people’s online behavior triggers an increase in the rate of clicking on the ads as well as higher likelihood of purchase ( Boerman et al., 2017 ), although the size of reported effects varies deeply between academic studies and claims made by advertising agencies.

How to Regulate Advertising?

An efficient and self-regulated market rests on the ability for firms to inform consumers about their novel products and stimulate them to buy those products. Yet, this should not be done at the expense of individuals’ mental, physical or financial health. Neither should marketing strategies drive consumers away from their explicit goals and intentions, such as staying on a diet or reducing their use of products with high environmental impact. While people with strong initial preferences are less likely to see their choice behavior dramatically influenced by marketing techniques, the latter are more efficient on individuals whose preferences have not yet formed such as children, vulnerable groups or individuals with conflicting motivations.

We believe that expanding our knowledge about decision mechanisms and how to modulate them is not inherently problematic as many beneficial applications, for individuals and for the society, can arise. The rehabilitation of addictive disorders is one important application. Nudging, which can be considered as the ‘good’ counterpart to marketing, relies on very similar theories and techniques to influence individuals’ behavior to make it more in line with their intentions. One previously mentioned example is the use of reminders to save money. Another example is the so called ‘green-nudging’ ( Schubert, 2017 ; Bonini et al., 2018 ) which prompts people to make ecologically responsible decisions. The key difference between marketing and nudging lies in the very idea of adequacy between the declared intentions of the customer (e.g., follow a specific diet, make ecologically responsible purchases) and the type of manipulation being exerted on their behavior. In addition, nudging is usually initiated by public institutions with the end goal of benefiting the society. For instance, nudging might encourage more ecologically responsible consumption by displaying the environment impact of products, but it will never orient consumers toward a specific brand. Public acceptability of nudging is generally positive ( Reynolds et al., 2019 ) while advertising made by companies motivated by profit is controversial. Therefore, the very idea of transparency from the part of the advertising company and consent from the customer seems crucial. Policy makers could consider empowering citizens by letting them decide whether they accept to be exposed to different types of advertising.

Strikingly, the legal system of several countries has adjudicated that promoting products which threaten public health should be prohibited. Advertisement of products containing tobacco or alcohol is strictly forbidden in many countries. In addition, the branding effect of cigarettes is reduced by including pictures of dramatic health consequences of smoking on packaging. Similarly, attempts to reduce the prevalence of obesity, diabetes and hypertension have been made by trying to limit the effectiveness of advertisements on high caloric food and beverages with associated warning messages. For instance, in 2007 in France, a law was adopted listing categories of nutritive products (e.g., sweets and sodas) whose advertisement had to contain a message suggesting to eat more fruits and vegetable, increase physical activity and reduce salt and sugar intake. Thus, the approach adopted so far to protect the population from potential detrimental effects of advertising focuses on specific products and age groups (mainly children). Nonetheless, as discussed earlier the potential damage of advertising extent to many groups of individuals.

A possibly efficient approach could be to limit the intrusive aspects of the advertising means, in order to allow vulnerable individuals, especially those with compulsive or addictive tendencies, to maintain self-protective strategies. Measures should be taken to prevent advertisement to be forced into the peripheral visual field of individuals attending a nearby focal point of interest. In order to avoid passive viewing, it could entail the prohibition of advertising messages in confined public spaces (e.g., bus stops) and in locations surrounding informative or salient focal point (e.g., information panels). One particularly striking example is the advertisement low-cost airplane companies place on the seat in front of their clients to incite them to buy snacks. Such practice is extremely intrusive as people cannot easily look away. Similarly, if advertisement in magazines would be on their own separate page, rather than next to an informative article, consumers would still have the opportunity of being informed of new products while controlling the degree of exposure to advertisement they are willing to accept. Internet ads could be forced in their own browser tab instead of being placed next to the focus of attention of users. A mandatory op-out option for specific categories of products would also be desirable to help individuals struggling with addictive behavior or eating disorders. The important aspect in this proposition is to allow consumers to regain control in their exposure to advertisement by having them consent to viewing ads through a motor action (such as clicking on the ads tab), rather than forcing passive viewing.

Due to our increasing knowledge of decision mechanisms and the increasing efficiency and outreach of communication means, marketing techniques are becoming both intrusive and powerful. The brain circuitry for decision and motivation changes during the lifespan or due to a diversity of contingent and individual factors. Because of our growing understanding of vulnerabilities to external influences, it is perhaps time to address the issue of intrusiveness of advertisement at a societal level and consider regulatory intervention.

Author Contributions

NB and ER prepared and validated the manuscript.

This work was funded by the European Research Council (ERC Consolidator Grant 617629).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• ^ A demonstration of the effect is available in a TEDx talk delivered by Antonio Rangel ( http://www.tedxcaltech.com/content/antonio-rangel ).

Agarwal, S., Driscoll, J. C., Gabaix, X., and Laibson, D. (2007). The Age of Reason: Financial Decisions Over the Lifecycle. Cambridge, MA: National Bureau of Economic Research, doi: 10.3386/w13191

CrossRef Full Text | Google Scholar

Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., and Wetzels, M. (2015). Unraveling the personalization paradox: the effect of information collection and trust-building strategies on online advertisement effectiveness. J. Retail. 91, 34–49. doi: 10.1016/j.jretai.2014.09.005

Armel, K. C., Beaumel, A., and Rangel, A. (2008). Biasing simple choices by manipulating relative visual attention. Judg. Decision Making 3, 396–403.

Google Scholar

Atkin, C. K. (1978). Observation of parent-child interaction in supermarket decision-making. J. Mark. 42, 41–45. doi: 10.1177/002224297804200406

Baumeister, R. F. (2002). Yielding to temptation: self-control failure, impulsive purchasing, and consumer behavior. J. Consum. Res. 28, 670–676. doi: 10.1086/338209

Baumeister, R. F., Heatherton, T. F., and Tice, D. M. (1994). Losing Control: How and Why People Fail at Self-Regulation , 1 Edn. San Diego: Academic Press.

Black, D. W. (2007). Compulsive buying disorder: a review of the evidence. CNS Spectr. 12, 124–132. doi: 10.1017/S1092852900020630

PubMed Abstract | CrossRef Full Text | Google Scholar

Boerman, S. C., Kruikemeier, S., and Borgesius, F. J. Z. (2017). Online behavioral advertising: a literature review and research Agenda. J. Advert. 46, 363–376. doi: 10.1080/00913367.2017.1339368

Bonini, N., Hadjichristidis, C., and Graffeo, M. (2018). Green nudging. Acta Psychol. Sin. 50, 814–826. doi: 10.3724/SP.J.1041.2018.00814

Calluso, C., Committeri, G., Pezzulo, G., Lepora, N., and Tosoni, A. (2015). Analysis of hand kinematics reveals inter-individual differences in intertemporal decision dynamics. Exp. Brain Res. 233, 3597–3611. doi: 10.1007/s00221-015-4427-1

Casey, B. J., Jones, R. M., and Hare, T. A. (2008). The adolescent brain. Ann. N. Y. Acad. Sci. 1124, 111–126. doi: 10.1196/annals.1440.010

Denburg, N. L., Cole, C. A., Hernandez, M., Yamada, T. H., Tranel, D., Bechara, A., et al. (2007). The orbitofrontal cortex, real-world decision making, and normal aging. Ann. N. Y. Acad. Sci. 1121, 480–498. doi: 10.1196/annals.1401.031

Eppinger, B., Hämmerer, D., and Li, S.-C. (2011). Neuromodulation of reward-based learning and decision making in human aging. Ann. N. Y. Acad. Sci. 1235, 1–17. doi: 10.1111/j.1749-6632.2011.06230.x

Ferraro, R., Bettman, J. R., and Chartrand, T. L. (2009). The power of strangers: the effect of incidental consumer brand encounters on brand choice. J. Consum. Res. 35, 729–741. doi: 10.1086/592944

Gaeth, G. J., and Heath, T. B. (1987). The cognitive processing of misleading advertising in young and old adults: assessment and training. J. Consum. Res. 14, 43–54. doi: 10.1086/209091

Galvan, A., Hare, T. A., Parra, C. E., Penn, J., Voss, H., Glover, G., et al. (2006). Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents. J. Neurosci. 26, 6885–6892. doi: 10.1523/JNEUROSCI.1062-06.2006

Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., et al. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. PNAS 101, 8174–8179. doi: 10.1073/pnas.0402680101

Gold, J. I., and Shadlen, M. N. (2007). The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574. doi: 10.1146/annurev.neuro.29.051605.113038

Goldberg, M. E., and Gorn, G. J. (1978). Some unintended consequences of TV advertising to children. J. Consum. Res. 5, 22–29. doi: 10.1086/208710

Gorn, G. J., and Goldberg, M. E. (1982). Behavioral evidence of the effects of televised food messages on children. J. Consum. Res. 9, 200–205. doi: 10.1086/208913

Halliwell, E., and Dittmar, H. (2004). Does size matter? the impact of model’s body size on women’s body-focused anxiety and advertising effectiveness. J. Soc. Clin. Psychol. 23, 104–122. doi: 10.1521/jscp.23.1.104.26989

Hare, T. A., Camerer, C. F., and Rangel, A. (2009). Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648. doi: 10.1126/science.1168450

Hare, T. A., O’Doherty, J., Camerer, C. F., Schultz, W., and Rangel, A. (2008). Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors. J. Neurosci. 28, 5623–5630. doi: 10.1523/jneurosci.1309-08.2008

Hare, T. A., Schultz, W., Camerer, C. F., O’Doherty, J. P., and Rangel, A. (2011). Transformation of stimulus value signals into motor commands during simple choice. Proc. Natl. Acad. Sci. 108, 18120–18125. doi: 10.1073/pnas.1109322108

Haryanto, J. O., Moutinho, L., and Coelho, A. (2016). Is brand loyalty really present in the children’s market? A comparative study from Indonesia, Portugal, and Brazil. J. Bus. Res. 69, 4020–4032. doi: 10.1016/j.jbusres.2016.06.013

Haushofer, J., and Fehr, E. (2014). On the psychology of poverty. Science 344, 862–867. doi: 10.1126/science.1232491

Henninger, D. E., Madden, D. J., and Huettel, S. A. (2010). Processing speed and memory mediate age-related differences in decision making., processing speed and memory mediate age-related differences in decision making. Psychol. Aging 25, 262–270. doi: 10.1037/a0019096

CrossRef Full Text | PubMed Abstract | Google Scholar

Itti, L., and Koch, C. (2001). Computational modelling of visual attention. Nat. Rev. Neurosci. 2, 194–203. doi: 10.1038/35058500

Karlan, D., McConnell, M., Mullainathan, S., and Zinman, J. (2016). Getting to the top of mind: how reminders increase saving. Manag. Sci. 62, 3393–3411. doi: 10.1287/mnsc.2015.2296

Karmarkar, U. R., and Plassmann, H. (2019). Consumer neuroscience: past. present, and future. Org. Res. Methods 22, 174–195. doi: 10.1177/1094428117730598

Kenning, P. H., and Plassmann, H. (2008). How neuroscience can inform consumer research. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 532–538. doi: 10.1109/TNSRE.2008.2009788

Krajbich, I., Armel, C., and Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 13, 1292–1298. doi: 10.1038/nn.2635

Krajbich, I., Lu, D., Camerer, C., and Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Front. Psychol. 3:193. doi: 10.3389/fpsyg.2012.00193

Krajbich, I., and Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proc. Natl. Acad. Sci. U.S.A. 108, 13852–13857. doi: 10.1073/pnas.1101328108

Laibson, D. (1997). Golden eggs and hyperbolic discounting. Q. J. Econ. 112, 443–478. doi: 10.1162/003355397555253

Lebreton, M., Jorge, S., Michel, V., Thirion, B., and Pessiglione, M. (2009). An automatic valuation system in the human brain: evidence from functional neuroimaging. Neuron 64, 431–439. doi: 10.1016/j.neuron.2009.09.040

Liberman, N., and Trope, Y. (2008). The psychology of transcending the here and now. Science 322, 1201–1205. doi: 10.1126/science.1161958

Lim, S.-L., O’Doherty, J. P., and Rangel, A. (2011). The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention. J. Neurosci. 31, 13214–13223. doi: 10.1523/JNEUROSCI.1246-11.2011

Löckenhoff, C. E., and Carstensen, L. L. (2007). Aging, emotion, and health-related decision strategies: motivational manipulations can reduce age differences. Psychol. Aging 22, 134–146. doi: 10.1037/0882-7974.22.1.134

Mani, A., Mullainathan, S., Shafir, E., and Zhao, J. (2013). Poverty impedes cognitive function. Science 341, 976–980. doi: 10.1126/science.1238041

Milosavljevic, M., Navalpakkam, V., Koch, C., and Rangel, A. (2012). Relative visual saliency differences induce sizable bias in consumer choice. J. Consum. Psychol. 22, 67–74. doi: 10.1016/j.jcps.2011.10.002

Navalpakkam, V., Koch, C., Rangel, A., and Perona, P. (2010). Optimal reward harvesting in complex perceptual environments. Proc. Natl. Acad. Sci. U.S.A. 107, 5232–5237. doi: 10.1073/pnas.0911972107

O’Donoghue, T., and Rabin, M. (1999). Doing it now or later. Am. Econ. Rev. 89, 103–124. doi: 10.1257/aer.89.1.103

Pärnamets, P., Johansson, P., Hall, L., Balkenius, C., Spivey, M. J., and Richardson, D. C. (2015). Biasing moral decisions by exploiting the dynamics of eye gaze. PNAS 112, 4170–4175. doi: 10.1073/pnas.1415250112

Pieters, R., and Wedel, M. (2004). Attention capture and transfer in advertising: brand, pictorial, and text-size effects. J. Mark. 68, 36–50. doi: 10.1509/jmkg.68.2.36.27794

Plassmann, H., Ambler, T., Braeutigam, S., and Kenning, P. (2007). What can advertisers learn from neuroscience? Int. J. Advert. 26, 151–175. doi: 10.1080/10803548.2007.11073005

Plassmann, H., and Karmarkar, U. R. (2015). “Consumer neuroscience: revealing meaningful relationships between brain and consumer behavior,” in The Cambridge Handbook of Consumer Psychology , eds M. I. Norton, D. D. Rucker, and C. Lamberton, (Cambridge, MA: Cambridge University Press), 152–179. doi: 10.1017/CBO9781107706552.006

Rangel, A., and Clithero, J. A. (2014). “The computation of stimulus values in simple choice,” in Neuroeconomics , ed. P. W. Glimcher, (Amsterdam: Elsevier), 125–148. doi: 10.1016/B978-0-12-416008-8.00008-5

Ratcliff, R. (1978). A theory of memory retrieval. Psychol. Rev. 85, 59–108. doi: 10.1037/0033-295X.85.2.59

Ratcliff, R., Smith, P. L., Brown, S. D., and McKoon, G. (2016). Diffusion decision model: current issues and history. Trends Cogn. Sci. 20, 260–281. doi: 10.1016/j.tics.2016.01.007

Reutskaja, E., Nagel, R., Camerer, C. F., and Rangel, A. (2011). Search dynamics in consumer choice under time pressure: an eye-tracking study. Am. Econ. Rev. 101, 900–926. doi: 10.1257/aer.101.2.900

Reynolds, J. P., Archer, S., Pilling, M., Kenny, M., Hollands, G. J., and Marteau, T. M. (2019). Public acceptability of nudging and taxing to reduce consumption of alcohol, tobacco, and food: a population-based survey experiment. Soc. Sci. Med. 236, 112395. doi: 10.1016/j.socscimed.2019.112395

Roitman, J. D., and Shadlen, M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489. doi: 10.1523/JNEUROSCI.22-21-09475.2002

Rose, S., and Dhandayudham, A. (2014). Towards an understanding of Internet-based problem shopping behaviour: the concept of online shopping addiction and its proposed predictors. J. Behav. Addict. 3, 83–89. doi: 10.1556/JBA.3.2014.003

Ruff, C. C., and Fehr, E. (2014). The neurobiology of rewards and values in social decision making. Nat. Rev. Neurosci. 15, 549–562. doi: 10.1038/nrn3776

Samanez-Larkin, G. R., and Knutson, B. (2015). Decision making in the ageing brain: changes in affective and motivational circuits. Nat. Rev. Neurosci. 16, 278–289. doi: 10.1038/nrn3917

Schubert, C. (2017). Green nudges: do they work? Are they ethical? Ecol. Econ. 132, 329–342. doi: 10.1016/j.ecolecon.2016.11.009

Schultz, W. (1998). Predictive reward signal of dopamine neurons. J. Neurophysiol. 80, 1–27. doi: 10.1152/jn.1998.80.1.1

Shah, A. K., Mullainathan, S., and Shafir, E. (2012). Some consequences of having too little. Science 338, 682–685. doi: 10.1126/science.1222426

Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., et al. (2008). Neurodevelopmental trajectories of the human cerebral cortex. J. Neurosci. 28, 3586–3594. doi: 10.1523/JNEUROSCI.5309-07.2008

Shimojo, S., Simion, C., Shimojo, E., and Scheier, C. (2003). Gaze bias both reflects and influences preference. Nat. Neurosci. 6, 1317–1322. doi: 10.1038/nn1150

Skurnik, I., Yoon, C., Park, D. C., and Schwarz, N. (2005). How warnings about false claims become recommendations. J. Consum. Res. 31, 713–724. doi: 10.1086/426605

Smith, A., Bernheim, B. D., Camerer, C., and Rangel, A. (2014). Neural activity reveals preferences without choices. Am. Econ. J. Microecon. 6, 1–36. doi: 10.1257/mic.6.2.1

Somerville, L. H., and Casey, B. (2010). Developmental neurobiology of cognitive control and motivational systems. Curr. Opin. Neurobiol. 20, 236–241. doi: 10.1016/j.conb.2010.01.006

Stanton, S. J., Sinnott-Armstrong, W., and Huettel, S. A. (2017). Neuromarketing: ethical implications of its use and potential misuse. J. Bus. Ethics 144, 799–811. doi: 10.1007/s10551-016-3059-0

Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Curr. Direct. Risk Decis. Making 28, 78–106. doi: 10.1016/j.dr.2007.08.002

Story, M., and French, S. (2004). Food advertising and marketing directed at children and adolescents in the US. Int. J. Behav. Nutr. Phys. Act. 1:3. doi: 10.1186/1479-5868-1-3

Sullivan, N., Hutcherson, C., Harris, A., and Rangel, A. (2015). Dietary self-control is related to the speed with which attributes of healthfulness and tastiness are processed. Psychol. Sci. 26, 122–134. doi: 10.1177/0956797614559543

Summerfield, C., and Tsetsos, K. (2012). Building bridges between perceptual and economic decision-making: neural and computational mechanisms. Front. Neurosci. 6:70. doi: 10.3389/fnins.2012.00070

Tobler, P. N., Fiorillo, C. D., and Schultz, W. (2005). Adaptive coding of reward value by dopamine neurons. Science 307, 1642–1645. doi: 10.1126/science.1105370

Towal, R. B., Mormann, M., and Koch, C. (2013). Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proc. Natl. Acad. Sci. 110, E3858–E3867. doi: 10.1073/pnas.1304429110

Tusche, A., Bode, S., and Haynes, J.-D. (2010). Neural responses to unattended products predict later consumer choices. J. Neurosci. 30, 8024–8031. doi: 10.1523/JNEUROSCI.0064-10.2010

Valkenburg, P. M., and Cantor, J. (2001). The development of a child into a consumer. J. Appl. Dev. Psychol. 22, 61–72. doi: 10.1016/S0193-3973(00)00066-6

van de Vijver, I., Ridderinkhof, K. R., and de Wit, S. (2015). Age-related changes in deterministic learning from positive versus negative performance feedback. Aging Neuropsychol. Cogn. 22, 595–619. doi: 10.1080/13825585.2015.1020917

van de Vijver, I., Ridderinkhof, K. R., Harsay, H., Reneman, L., Cavanagh, J. F., Buitenweg, J. I. V., et al. (2016). Frontostriatal anatomical connections predict age- and difficulty-related differences in reinforcement learning. Neurobiol. Aging 46, 1–12. doi: 10.1016/j.neurobiolaging.2016.06.002

van Hoorn, J., Fuligni, A. J., Crone, E. A., and Galván, A. (2016). Peer influence effects on risk-taking and prosocial decision-making in adolescence: insights from neuroimaging studies. Curr. Opin. Behav. Sci. 10, 59–64. doi: 10.1016/j.cobeha.2016.05.007

Wilcox, B. L., Kundel, D., Cantor, J., Dowrick, P., Linn, S., and Palmer, E. (2004). Report of the APA Task Force on Advertising and Children. Washington, D.C: American Psychological Association, doi: 10.1037/e539692009-001

Zauberman, G., and Urminsky, O. (2016). Consumer intertemporal preferences. Curr. Opin. Psychol. 10, 136–141. doi: 10.1016/j.copsyc.2016.01.005

Keywords : value-based decisions, choice biases, marketing, regulation, decision neuroscience

Citation: Bault N and Rusconi E (2020) The Art of Influencing Consumer Choices: A Reflection on Recent Advances in Decision Neuroscience. Front. Psychol. 10:3009. doi: 10.3389/fpsyg.2019.03009

Received: 26 August 2019; Accepted: 19 December 2019; Published: 21 January 2020.

Reviewed by:

Copyright © 2020 Bault and Rusconi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nadège Bault, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Pharmacy (Basel)

Career Preference and Factors Influencing Career Choice among Undergraduate Pharmacy Students at University of Khartoum, Sudan

Ahmed h. arbab.

1 Department of Pharmacognosy, Faculty of Pharmacy, University of Khartoum, Khartoum 11111, Sudan; [email protected]

Yasir A. M. Eltahir

2 Department of Respiratory Care, College of Applied Sciences, Almaarefa University, Riyadh 11597, Saudi Arabia; as.ude.tscm@rehatY

3 Department of Anatomy, Faculty of Medicine, Kordofan University, Elobeid 51111, Sudan

Fatima S. Elsadig

4 Faculty of Pharmacy, University of Khartoum, Khartoum 11111, Sudan; moc.liamg@501bahihsamitaF

Bashir A. Yousef

5 Department of Pharmacology, Faculty of Pharmacy, University of Khartoum, Khartoum 11111, Sudan

Associated Data

All data used and analyzed during the current study are available from the corresponding author on reasonable request.

The pharmacy profession has expanded and adapted to changes in community needs. Although career planning and understanding the determinants of career choice are essential, there remains a lack of studies exploring factors influencing future career plans. This study was conducted to identify career preferences and factors influencing future career choices among undergraduate pharmacy students. A cross-sectional study was carried out at the Faculty of Pharmacy, University of Khartoum. A self-administered questionnaire was used to collect data from randomly selected participants. Out of 220 respondents, 85.9% were females. The average age of the respondents was 21.7 ± 1.5 years. Clinical pharmacy was selected as the most preferred future career domain (30%), followed by academia and research (12%), the pharmaceutical industry (11%), and community pharmacy (10.5). Approximately 20% of participants reported a preference for moving abroad for work. Regarding factors influencing future career domain choice, participants ranked training in the workplace (80%) and curriculum content (70%) as the top faculty-related factors, while interaction with practicing pharmacists (71.8%) and salary (78%) were the major personal-related and job-related factors. This study emphasized the importance of understanding job preferences and the factors influencing career choice, and could be useful in ensuring a future balance between professional domains and meeting society’s evolving expectations.

1. Introduction

The pharmacy profession has transformed and adapted itself to changes in the health care system and social needs. It has expanded from a drug-focused profession to include patient care and service-driven professions [ 1 , 2 ]. Until the twentieth century, pharmacists’ responsibilities were limited to compounding, quality control, and dispensing. In 1997, the World Health Organization (WHO) introduced the ‘seven-star pharmacist’ concept, covering the roles each pharmacist must perform: caregiver, decision-maker, communicator, manager, lifelong learner, teacher, and leader [ 3 ]. Two criteria, ‘researcher’ and ‘entrepreneur’, were added later, which culminated in the ‘nine-star pharmacist’ concept [ 4 ]. In 2000, the seven-star pharmacist concept was adopted in the International Pharmaceutical Federation (FIP) policy statement on good pharmacy education practice [ 5 ]. Currently, in addition to their classical roles as drug specialists, pharmacists work in multi-disciplinary settings to deliver pharmaceutical care. As a part of the health care system, their roles involve patient-oriented services, patient education, and counseling about medication and patients’ quality of life [ 6 ].

In Sudan, the pharmacy profession is locally governed by the Sudan Medical Council and Directorate General of Pharmacy. Undergraduate pharmacy education lasts for 5 years, and the student acquires a Bachelor of Pharmacy (B. Pharm) degree upon completion. This is followed by a mandatory one-year internship in a governmental pharmacy sector, and then a license is issued after passing an exam conducted by the Sudan medical council. About 67% of the pharmacist workforce is employed in private retail pharmacies; 19% are employed in the public sector, including hospitals and regulatory bodies; and less than 2% are employed within pharmaceutical manufacturing [ 7 ]. This situation highlights the need for policies that will promote equitable distribution of pharmacists among different career domains to meet community needs. Choosing a career domain within the pharmacy profession is not a straightforward process. It is influenced by internal faculty-related factors, personal/family-related factors, and career domain-related factors [ 8 ]. Although the role of the pharmacy professional has expanded, it is often observed that pharmacy students do not select preferences until they have graduated. Increasing students’ awareness about future career planning could help to achieve goals in a successful manner.

Career planning and understanding the factors influencing career decisions are crucial to facilitate students’ improvement in the area they are interested in, and will be used in their future professional life. Globally, in the United States, the Accreditation Council for Pharmacy Education (ACPE) mentions the need for recruitment policies, as part of their document on accreditation standards [ 9 ]. In Sudan, Standards for Accreditation of Medical Schools (SAMS), which are based on World Federation for Medical Education (WFME) standards, include career guidance and planning as a quality development standard for accreditation of pharmacy schools [ 10 ]. Moreover, according to World Bank statistics, Sudan spends about 2.2% of its limited gross domestic product (GDP) on education [ 11 ].

There should be a well-thought-out link between education and career progression, particularly in pharmacy colleges, due to the high diversity of pharmacy career domains and high cost of pharmacy schools. The current situation in Sudan indicates that undergraduate students who do not determine their future career orientations before graduation are beginning to work in an unplanned way after graduation, and consequently, this results in a loss of interest, low productivity at work, or even failure if they choose a profession that is incompatible with their abilities. Thus, understanding the factors that influence undergraduate pharmacy students’ choice of a particular professional domain will help undergraduate pharmacy students develop an accurate perception of pharmacy profession domains and make information-based decisions about their career choice. This could enhance recruitment strategies, job satisfaction, and retention as well as productivity. Thus, the current study was carried out to identify job preferences and the decision-influencing factors among undergraduate pharmacy students at the University of Khartoum.

2. Materials and Methods

2.1. study design and setting.

A descriptive cross-sectional study was conducted at the Faculty of Pharmacy, University of Khartoum, Sudan. The Faculty of Pharmacy was established in 1964 and remained the only one in Sudan for about three decades. The study was conducted from March to May 2021.

2.2. Study Population

The study population was undergraduate pharmacy students. Only the third-, fourth-, and fifth-year students who were registered and undertook courses in the Bachelor of Pharmacy program during the study period were included in the study. First- and second-year students are not included in the study as in the first two years of the syllabus focus on basic medical and pharmaceutical science; thus, they have yet to establish enough awareness about pharmacy profession domains.

2.3. Sample Size and Sampling

The sample size was calculated using “Raosoft”, a sample size calculation software product, with 95% confidence intervals and a 5% margin of error with an expected response distribution of 50% [ 12 ]. Based on the data obtained from the Faculty of Pharmacy, the accessible study population was 454 students (third year: 180 students, fourth year: 95 students, and fifth year: 179 students). The minimum sample size required was 209 students (third year: 83 students, fourth year: 44 students, fifth year: 82 students). Two probability sampling methods were used to select the participants: stratified and systematic sampling. The study population was divided into three strata according to the academic year of study, and then a sample size appropriate to stratum size was obtained separately from each stratum using systematic sampling. The first unit of each stratum was randomly selected.

2.4. Data Collection

A self-administered structured questionnaire was used to collect data. The questionnaire was adapted from the previous studies undertaken using a questionnaire with confirmed reliability (pilot study) and internal consistency (Cronbach’s α > 0.7) [ 8 ], and it covered pharmacy students’ career preferences and factors influencing career choice [ 8 , 13 ]. The questionnaire consisted of three parts: the first part explored the socio-demographic characteristics of the participants; the second part contained one question and 12 options investigating the career domain preferred by students; and the third part consisted of 16 questions/items designed to access factors influencing future career domain choices. These factors were arranged into three themes: faculty-related influences (curriculum course/subject content, faculty extracurricular activities, a faculty member’s advice, and visits to a workplace); personal-related influences (family members’/relatives’ advice, a family member’s career choice, a friend’s career choice, good social status, and interaction with a practicing pharmacist); and job-related influences (opportunity for self-employment, an opportunity for part-time work, an opportunity for promotion and advancement, opportunity for health insurance, job salary and incentives, job allowances, and training in a workplace). A five-point Likert scale ranging from strongly agree to strongly disagree was used to rate the participants’ responses to the third part of the questionnaire. Two senior experts revised the questionnaire to ensure its validity. The questionnaire was also pre-tested with selected students to check the validity of the questions. Suggestions obtained from these experts and students were considered as amendments in preparing the final draft. The data from the pretest were not included in the final study.

A web-based Google form was used to create the online questionnaires that were automatically hosted via a unique uniform resource locator (URL). The URL link ensured the confidentiality of data and gave participants access from anywhere via their personal smartphone, laptop, or desktop computer. Preselected study participants were invited individually to participate through their contact information. Responses were collected from 23 March 2021 to 17 April 2021 and automatically sorted in a “Google Drive” database.

2.5. Data Analysis

The Statistical Package for Social Sciences (SPSS) version 26 software (IBM Corporation, Armonk, NY, USA) was used to analyze the data. The chi-square test was used to examine significant difference or association between independent socio-demographic variables (gender, year of study) and dependent variables. Data with a p -value of 0.05 or less were considered statistically significant.

3.1. Demographic Characteristics of the Respondents

Out of 220 pharmacy students enrolled in the study, 189 (85.9%) were female. The average age of respondents was 21.7 ± 1.5 years, with a range of 19 to 29 years. About 38.6% of respondents were in the fifth year, 23.2% were in the fourth year, and 38.2% were in the third year of study.

Studying pharmacy was the first preferred choice for 161 (73.2%) of respondents at the time of application to universities, with insignificant associations between pharmacy as a first-preferred program, gender, and study year ( Table 1 ).

Participants’ choice of pharmacy as the first-preferred program in association with their demographic characteristics.

3.2. The First-Choice Career Domain of Participants

Clinical pharmacy was selected as the most-preferred career domain after graduation (n: 64; 29.9%), followed by academia and research (n: 26; 11.8%), the pharmaceutical industry (n: 24; 10.9%), community pharmacy (n: 23; 10.5%), and public health (n: 14: 6.4%). Drug quality control, medical representatives, and drug regulatory bodies were marked as the least-preferred career domains by 10 (4.5%), 7 (3.2), and 1 (0.5%) of the respondents, respectively. Importantly, about 20% of participants preferred to move abroad for work. Moreover, data analysis revealed a significant association between gender and preferred career domain ( p -value: 0.015) ( Table 2 ).

Association between preferred career domain and demographic characteristics.

* Aca & Res.: Academia and research, Clin. ph.: Clinical pharmacy, Com. ph.: Community pharmacy, Drug Q.C., Drug quality control, Drug reg.: drug regulatory bodies, Med. rep.: Medical representatives, Pha. ind.: Pharmaceutical industry, Pub. Hea.: Public health, W. out.: Working outside Sudan, No. w.: Not working. ** Significant difference between the compared groups at p -value < 0.05.

3.3. Factors Influencing Future Career Choice

Factors influencing future career domains were broadly arranged into three categories: faculty-related factors, personal/family-related factors, and job-related factors. Out of five faculty-related factors, 178 (80.9%) of respondents strongly agreed or agreed that training in a workplace (pharmacy, industry, etc.) influenced career domain choice. Regarding personal/family-related factors, 158 (71.8%) and 140 (63.6%) of respondents strongly agreed or agreed that interaction with practicing pharmacists and good social status influenced career domain choice, respectively. On the other hand, 171 (77.7%) and 162 (63.6%) of the respondents either strongly agreed or agreed that job salary and job allowances, respectively, influenced career domain choice. Furthermore, chi-square analysis revealed that gender was insignificantly associated with influencing future career domain choice decisions ( Table 3 ). Significant associations, with p -values of 0.024 and 0.017, were found between the influence of a family member’s career choice or interaction with practicing pharmacists, respectively, and the year of study ( Table 4 ).

Association between gender and different factors influencing career choice.

Association between the year of study and different factors influencing career choice.

* Significant difference between the compared groups at p -value < 0.05.

4. Discussion

Exploring students’ preferences toward different future career domains and their motivational variables is essential to designing and implementing future career orientation programs. To our knowledge, this is the first study that attempted to assess preferred career domains and factors influencing career domain choice decisions in Sudan. Results of this study highlighted the high female-to-male ratio (86%:14%). This finding is similar to those in many studies conducted among pharmacy students in Jordan [ 8 , 14 ]. Saudi Arabia [ 15 ], and Malaysia [ 16 , 17 ]. Since admission to the faculty is based on students’ academic achievement via the Sudanese secondary school certificate, and over 50% of admitted students are female, the high female-to-male ratio could be attributed to the fact that top-ranked female students prefer health sciences. Moreover, female students demonstrate higher academic achievement on Sudanese secondary school examinations than male students [ 18 ].

Nearly three-quarters of respondents indicated that studying pharmacy was their first-preferred choice. This result is similar to those concluded in studies conducted in South Africa [ 13 ] and the United Kingdom [ 19 ]. However, the current study finding is much higher than reported in studies conducted in Sierra Leone, where one-quarter of the respondents chose pharmacy as the first study field [ 20 ]. Furthermore, it is higher than the findings of a survey in Saudi Arabia, where about 40% of respondents selected the study of pharmacy as the first choice [ 21 ]. It is expected that students who attain high academic achievement in secondary school studies largely choose to study medicine as their first choice, with pharmacy or another health-related program as a second choice [ 13 , 22 ].

Regarding students’ future career domain of practice preference, our study showed that working as a clinical pharmacist was the most desired career domain (29.9%). The strong desire of respondents for practicing clinical pharmacy could be attributed to the ambition of students to keep pace with recent advancements in the pharmacy profession. In addition, the availability of work opportunities with good salaries, particularly abroad, may positively influence participants to prefer this field. In 2008, the American College of Clinical Pharmacy (ACCP) developed the core competencies of the clinical pharmacist. The proposed core competencies were, in brief: optimization of medication therapy, promotion of health, wellness, and disease prevention [ 23 ]. Fortunately, the B. Pharm curriculum was changed in 2016 from a traditional focus on pharmaceutical science courses to a modern curriculum that integrates more pharmacy practice and clinical pharmacy courses. Moreover, a clinical pharmacy training unit was established at Soba University Hospital [ 24 ]. Reform of the curriculum will help to produce future pharmacists with competency in providing patient care in collaboration with physicians and other health care providers. Currently, the level of clinical pharmacy services provided is low, and collaborative measures and support from health care professionals are needed to overcome the challenges and improve clinical pharmacy practice in Sudan.

The second most preferred career domain was academia and research (11.8%), which differs from the results of Ethiopian [ 25 ] and South African [ 13 ] studies, where academia and research attracted 16% and 9.2% of respondents, respectively. Our findings, on the other hand, partially agree with those of studies conducted in Jordan [ 14 ], Saudi Arabia [ 15 ], and Australia [ 26 ], where academia and research are the most popular career paths. The main motivators toward academia and research include favorable opportunities for professional development, the chance to shape the future of pharmacy, the autonomy of the positions, and the flexible working environment. In addition, student participation in teaching and research via student-centered active learning may further attract students to this field [ 27 ].

The third-preference future career options for respondents were the pharmaceutical industry (10.9%) and community pharmacy (10.5%). This study’s findings are in parallel with the findings of a study conducted among Malaysian pharmacy students [ 16 ], but not in line with the findings of two studies conducted in Saudi Arabia, where they reported the pharmaceutical industry and community pharmacy as the least preferred career domains [ 15 , 28 ], or the findings of a study among Iraqi pharmacy students, where community pharmacy was ranked as the first-choice future career option [ 29 ]. This finding is significant because it contradicts the current distribution of the pharmacist workforce in these domains; according to literature, approximately 67% of pharmacists work in private community pharmacies, while less than 2% are employed in the pharmaceutical industry sector [ 30 , 31 ]. Relatively low preferences for community pharmacies could indirectly impact the reported low levels of job satisfaction among community pharmacists [ 7 , 32 ]. Since private pharmacies are the most available workplace for pharmacy graduates, efforts should be directed to design and apply policies to improve community pharmacy job satisfaction and performance.

The study revealed that medical sales representative was an undesirable career domain, as it was chosen by only 3.2% of respondents. This finding contradicts the studies conducted in Jordan and Iraq [ 14 , 29 ], in which participants ranked medical representatives among the top-three preferred future career domains. Globally, most pharmaceutical companies allocate a relatively high budget for employing and training medical representatives; pharmaceutical product promotion and marketing expenditure is higher than research and development expenditure [ 33 ]. The negative attitude toward medical sales representatives could be an indirect consequence of the prolonged drug shortage in Sudan since the COVID-19 pandemic lockdown measures, further aggravated by local currency inflation [ 34 ], with the lockdown and economic instability making it challenging for many pharmaceutical companies to thrive.

The study also indicates that drug regulatory bodies were undesirable career domains, as they were chosen by only 0.5% of respondents. This finding was consistent with studies conducted in Malaysia [ 18 ], and Jordan [ 8 , 22 ], all of which found the drug regulatory affairs domain to be one of the least-preferred options. A drug regulatory body is a relatively new profession that governments have established to control the safety and efficacy of pharmaceutical products and medical devices [ 35 ]. The low preference of participants for some future career domains, such as drug regulatory bodies, may be due to a lack of sufficient knowledge and awareness about these career domains. The regular revision of the curriculum, providing career ordination, and workplace training programs are crucial to make students aware of various pharmacy profession opportunities, and the importance of their role in different career domains.

As reported in other medical professions in Sudan, approximately 22% of participants wish to migrate, and these findings are not surprising, as reported in other medical professions in Sudan [ 35 , 36 ]. A global report pointed to a shortage of pharmacy professionals in Africa, particularly in low-income developing countries [ 37 ]. The massive brain-drain of health professions has a negative impact on services in the country. Therefore, efforts should be focused on managing migration [ 36 ].

Gender may affect the selection of the response to the most preferred career options. Sudan, as with other Arab communities, is a conservative society in which females prefer to work in a place with flexibility and fewer working hours. In the current study, a relatively large percentage of females preferred to work in academia and research (22.7%) and community pharmacy (11.6%), while the males’ preference for these domains was 6.5% and 3.2%, respectively. On the other hand, a high proportion of males showed a preference to working as medical sales representatives (13%), compared to females (1.6%). This finding agrees with reports from Jordan [ 8 ], and Saudi Arabia [ 38 ], where the influence of gender on future career choice was observed and attributed to cultural and social reasons [ 38 ]. Fieldwork, outstation trips, night shifts, and weekend hours that may be required to complete tasks, make the medical representative position more appealing to men [ 8 ].

When investigating the motivational factors behind the students’ choice of a particular pharmacy career domain, data analysis revealed that the key faculty-related factor was training in a workplace (around 51% strongly agree, 30% agree), followed by a curriculum/course at college (around 70% agree), which is consistent with a study conducted in Saudi Arabia reporting that previous training in a hospital and in community pharmacy had a significant impact on student future career choice [ 28 ]. This finding draws attention to the importance of workplace-based learning/training for students. Workplace training allows the student to apply their knowledge and gain social, cultural, and professional values; this implicit sort of workplace-based learning is known as the hidden curriculum, and has been identified as a significant issue in health professional education [ 39 ].

Personal/family-related influences and interaction with practicing pharmacists were ranked as the top factors by 72% (30% strongly agree, 42% agree), while a family member’s career choice and a friend’s career choice were ranked as the minor motivational factors (only 7.5% and 26% of participants respectively either agree or strongly agree). This finding contrasts with a study conducted in the United Arab Emirates, which reported the minimal influence of pharmacists as role models on students’ career selection [ 40 ]. In agreement with our findings, a study conducted in Saudi Arabia reported the influence of friends and family as minor motivational factors, at 16.5% and 18.5%, respectively [ 15 ]. Importantly, the association between the influence of training in a workplace and the year of study was statistically significant ( p -value 0.01).

The job salary and incentives (78%), followed by the opportunity for promotion and advancement (75%), were the most important job-related factors influencing future career domain choice. This finding is consistent with studies from Saudi Arabia [ 15 , 28 , 41 ]. In contrast, a similar study in the United States concluded that the job environment was the most important factor influencing career decisions [ 42 ]. There was no significant association between socio-demographic characteristics and job-related factors ( p -value > 0.005)

5. Limitations

There were some limitations to this study. It was conducted in one university; thus, it cannot be generalized to pharmacy students in other universities. It was also a cross-sectional study and administered to the students at one point in time. However, students’ choices may change with exposure to experiences; repeating the survey as students progress may enable evaluation of the consistency of students’ career choices and motivational factors. Additionally, the option ‘working outside Sudan’ was written under pharmacy career domains, not in a separate section.

Despite these limitations, our study is novel as it is the first report that assessed the views of pharmacy students towards preferred career domains in Sudan. The findings of the study will inform and guide university authorities, Sudanese pharmaceutical societies, and other stakeholders about the factors that affect students in choosing a pharmacy career domain. It is recommended that the gap between the implemented curriculum and employment skills should be narrowed through auditing and regular updates of the curriculum. In addition, establishing training programs in collaboration with governmental and private bodies can further increase students’ awareness about career domains. Moreover, organizing activities, such as career days, symposiums, and workshops, for various areas of pharmacy professions, particularly for arising career domains, will enable students to identify and achieve their future career goals.

6. Conclusions

The present study highlighted a baseline understanding of the career preference and main factors influencing future career domain choice among undergraduate pharmacy students at the University of Khartoum. The study showed a positive attitude in most students towards pharmacy when applying to the program. Clinical pharmacy, academia and research, the pharmaceutical industry, and community pharmacy were the most preferred choices of students. The main factors that influenced career preference were training in a workplace, curriculum content, interaction with practicing pharmacists, job salary and incentives, and the opportunity for promotion and advancement.

Author Contributions

A.H.A.: conceptualization, data curation, data analysis, writing the original draft; Y.A.M.E.: supervision, conceptualization, resources, software, reviewing, and editing; F.S.E.: conceptualization, data curation, data analysis; B.A.Y.: supervision, conceptualization, investigation, data analysis, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Institutional Review Board Statement

The study was conducted in agreement with the recommendations of the Declaration of Helsinki, and approved by the Ethics Committee of the Sudan Medical Specializations Board (SMSB), Khartoum, Sudan (MHPE-B2, 15 March 2021).

Informed Consent Statement

Written, informed consent was obtained from all subjects involved in the study separately and voluntarily after clearly explaining the purpose of the study, and the confidentiality of the data was maintained.

Data Availability Statement

Conflicts of interest.

The authors declare no relevant conflicts of interest or financial relationships.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Factors Influencing Students’ Career Choices: Empirical Evidence from Business Students

Journal of southeast asian research.

Kazi Afaq Ahmed, Nimra Sharif and Nawaz Ahmad

 institute of business management, karachi, pakistan, academic editor: mumtaz bte ahmad, cite this article as: kazi afaq ahmed, nimra sharif and nawaz ahmad (2017)," factors influencing students’ career choices: empirical evidence from business students ", journal of southeast asian research, vol. 2017 (2017), article id 718849, doi: 10.5171/2017.718849, copyright © 2017. kazi afaq ahmed, nimra sharif and nawaz ahmad . distributed under creative commons cc-by 4.0.

An incorrect career choice directs all individual efforts and resources into a wrong direction, when not aligned with the expectations; would not only be frustrating rather draining of the individual energy and wastage of resources. The motivation behind this research study was to investigate the factors that influence the career selection choice of the student and create a possible alignment between their preferences and the institutions curriculum and offerings. The structured questionnaire was distributed among MBA/BBA students enrolled in different universities of Karachi. Data were collected from 120 participants and analyzed using SPSS. Correlation and multiple -regression were applied as statistical tools to test the hypotheses. The results of the study revealed that “interest in the subject” is the most dominant factor influencing career choices of business students f (1,118)= 12.304, p<0.05, R=.307. Financial outcomes, ease of subject and future job opportunities were observed to have minor impact. The interest in the subject is also related and has some linkage with personality type. Mismatch of the personality and lack of interest in the subject is dangerous, and could end up into disastrous results in terms of student dissatisfaction, demotivation, lack of productivity leading to increased dropouts and career failure. The study results are indicative of the importance of students counseling sessions and other interventions to provide them with updated knowledge, and information to create their interest in the right choices and available options. The career choice of the students is also influenced by the level of their social class, financial resources, affordability and future employability.

Introduction

The right career choice for the students entering into the professional education is critical having high impact on their professional life and future achievement. This is the turning point: it cannot be left, on intuition, preconceived notions, wild imaginations or popular concepts. A miss-perceived career choice directs all individual efforts and resources into wrong direction, when not aligned with the expectations; would not only be frustrating rather draining of the individual energy and wastage of resources. The re-alignment is possible, but it has serious implications in terms of time, money and motivation. The career choice of the students must need to be based on; strong knowledge, complete information, and appropriately guided, matching individual personality type and other intrinsic and extrinsic factors. The students need to be oriented on new emerging trends, future opportunities and challenges in the context of career choice options. They need to know the prevalent market trends and practices and job scenario of various sectors. 

This study is focused on students enrolled in business discipline; the courses which are offered to business students are MBA, BBA, and BS etc. The business students are those who obtained a university degree in Business Administration. They obtained degree in Business Administration with major courses in marketing, banking and finance, human resource management, management information system, supply chain management, and some students do bi-majors that are specialized in two fields simultaneously. However their career choice for selection of the particular specialization needs to be based on complete information and market place practices. The institutes regularly hold sessions with the corporate professionals, companies and trade bodies to create interface with the students. The interface with the industry professional is extremely important, it provides the students with the opportunity to discuss and clarify their thought process and align their perception about various disciplines and career options with the actual ground realities.  The universities have the student counseling department where the career guidelines are provided to the students to help them understand the various disciplines and select the one that matches their aspirations, interest and personality type. The success of the young leaders in their career endeavors depends on the alignment of their career choice with their interest and personal preferences.

Choice of career is standout amongst the most noteworthy determinations for business students that will be supportive to them in their future thoughts. The significance of picking a specialization in Administration and any courses accessible is a key and essential part for each business student as it will be the building pieces of achievement later on. One must be more learned about the way they will be taking for this choice. Numerous variables influence career decisions of secondary school students. Recognizing these variables would give individuals, instructors, and industry a thought as to where to put their trust in the profession choice procedure which incorporates organization, financial firms or enterprise, human asset administration, data framework, hierarchical conduct, advertising, research routines, and so on. The components that impact while selecting business institute incorporates area, picture, workforce, research opportunities, industry linkages, and promoting, expense structure, and so forth. These business students subsequent to getting degree begin their careers in real business world. Also there would be business students who therefore choose what the right way is for them inside of the time compass of the first year and afterward there are the slow learners who take very late choices. The orientation of this choice will affect the business students’ life for no more less than 30 years.

Scope of the Study

The scope of the research study is focused on the students enrolled in business institutes and universities in business administration and studying various courses in their BBA /MBA program. They yet have to pass through the process of making their career choices by selecting the specialized area within business discipline. This study would have a more prominent position for the master students to get themselves prepared and to sorts out other than occupation, the linkage with the experts involved in business firms arranging and planning endeavors for the fresh business graduates to help them in correct career choice.

Objectives and Significance of Study

The objective of this research study is to investigate the factors that influence the career choice decision of the business discipline students. The study results would be helpful for the student counselors, parents, and universities, in developing the career counseling and guidance programs for the students to facilitate them in making the right career choice. The study was focused on the following key areas:

• To determine the influences on the decision making process of management science students in their career selection.
• To derive the relative weight of each factor and see which factor is more influential?
• To divert the attentions of academic researchers and influential people towards how the human capital of our country makes their career selection decision.

Statement of Problem

Career selection decision making process is one of the key elements in an individual’s life (Alberts et al. 2003). There exist numerous problems encountered by students in their process of career selection (Olamide and Olawaiye, 2013). Wrong career selection opens the door for life-long consequences (Mashige and Oduntan, 2011).Such individual’s by underperforming becomes a source of inefficiency not only for themselves, their organization but also for the economy as a whole. (Issa and Nwalo, 2008).

Pakistan is a developing country with minimum GDP growth rate of 4.71% (economic survey of Pakistan, 2016) and HDI ranking of 147 out of 188 countries worldwide (UNDP, 2014-15) If the core educated population of the country enrolled in business schools would not be taking right career decisions, then the country would not be able to boost up itself in order to compete in such complex and dynamic environment.

Literature Review

Review of the literature will be explored within this chapter that evaluate and contrast research in factors that influence career choice decisions and the extrinsic and intrinsic factors will be discussed in detail with the help of current research studies. Specifically, the literature will reveal what factors are important in making career choices by the management sciences students and which factors are impactful in different contexts and cultures in the world. These factors eventually all guide to the awareness that career choice is not a lineal process but that it has factors that are influential in terms of making future plans and those factors themselves are influenced by the surroundings, external environment, country situation, family orientation and personal interests.

The word career has been a derivative of French and Latin origin. Its simplest definition is given by Geciki (2002) as; the occupational, commercial or industrial activity that a person may adopt during his educational life or in some other part or till his death. Redman and Wilkinson (2001) clarifies career as the application of a person’s cognition and capabilities, providing command over profession, timely work expertise and a basis of developing and bettering business networks. Individuals chose career planning to pursue the professional objectives, getting informed about upcoming opportunities, their results and their timely evaluations. It is considered to be a beginning in the stages of career choices but still of paramount significance. People prefer the career that could provide them sound basis for an improved standard of living (Cavus, Geri and Turgunbayeva; 2015). Career planning done on individual basis is a plan as to how an individual would foster in his profession. As regards the organizational level,  it talks about promotional aspects together with personal development quadrant. So briefly career planning is the process whereby the individual himself or his organization helps in pursuing his growth objectives in conformity with his expertise in the area, capabilities and aims (Bayraktaroğlu, 2011).

Choices that people make related to their career can be categorized to be influenced by two factors that are psychological and social. Social factors are part of an individual’s social bonds, their parents, family, history and other characteristics of their environment. Psychological factors can be an individual’s perception, cognitive and effective intentions, beliefs, ideas, personality and assessments related to forthcoming business environment (Ozen, 2011). Different inquiries on the life of the students have come up with different findings. The results of a quantitative study conducted in central Pennsylvania by taking rural young adults and adolescents as respondents indicated that influence exerted by an individual’s family, society, state of economy, their interpretation of better job and financial constraints were major reasons that can impact their career selection (Ferry; 2006). A systematic review of 600 articles published in 2003-2013 of low-income countries conducted by Puerto’s EB (2013) determined intrinsic factors amongst medical students (age, sex, rural background) and extrinsic factors (salaries, governmental institutions, medical institutes reputation, training techniques) influence a medical student’s decision to choose a career in primary care; and to establish that some factors were different among students in high-, middle- and low-income countries.

Interest in the subject

Studies conducted in different countries different cultures can sum up with different relationships among variables selected for the study; for example, in Kenya personality types and interest in subject is a factor that impacts lot on making career choices by the students, but if we look at this similar kind of study conducted in South Africa where demographics and culture are totally different it, revealed that the financial factors impact on career choices of students, Fatima Abrahams et al (2015). Zing (2007) research concluded that personal liking of an individual towards a particular subject contributes in his career selection decision. Shertzer and Stone (2003) found that interest depicted by students in some subjects will mostly lead to the better examination performance and selection of profession in the same direction. Alexander et al (2011) examination of students enrolled in the disciplines of information technology found that liking of the subject has chief significance when looking at the factors contributing in professional direction adopted by students. Edward and Quinter (2012) investigation disclosed that an individual’s proclivity towards a particular field or subject, its predilection for a particular job and match between his personality and selected professions is an important factor contributing in career path.

It is apparent that a relationship exists between personality and career interest in a particular subject. There is also personality differences in career choices amongst students, stated by the study conducted on students of Institution of Technology (IOT) in Ireland. Statistics showed that there is three times higher ratio of dropouts of adolescents from the course/subjects than from university, Higher Education Authority (HEA) evidenced that this is because of wrong initial career choices. Clement H (2014) investigated one of the major factors is the mismatch of personality with a course/career. In the process of making career choices, personality plays a significant role; productivity, fulfillment and motivation are directly related to the individual. Lack of fit can be the most dangerous cause of dissatisfaction and ends up in to the stress career failure. Rebecca J. et al (2016) conducted a study on 399 students in Kenya which resulted in that there is a relationship between personality types, and career choice. Most of the students were satisfied with the course they selected before entering the university which indicates that suitable career choice for students would improve satisfaction and success in their course of study and future employment. But on the other side, when students make changes in their course section it indicated that the choices of subject selection did not go in line with their future career choices. Consequently, it constructs probability of the status of the relationship between personality types and career choice among undergraduate students in Kenya. The study revealed that there is a significant relationship between personality types and career choices among undergraduate students. Christine (2005) study conducted in South Africa on 770 students to determine the relationship between the personality traits and career choices, and because of cultural and environmental change the relationship seems weaker as compared to other countries. 770 respondents completed the Sixteen Personality Factor Questionnaire (16PF) and the Interest Questionnaire (INQ). Partial correlations showed that gender and race may influence these relationships, however these were slight changes.

Future Job Opportunities

Career growth, is an ongoing process for some people;they get engaged in different jobs through choosing amongst job opportunities available in the market. Every person undertaking the procedure of choosing opportunities subjective by many factors, context they live in, personal aptitudes, and educational skills (Bandura, Barbaranelli, Caprara, & Pastorelli, 2001). It is natural that people always try to forecast or direct sometimes the future which is uncertain, so as students, always try to plan their careers for a secure future where superior job opportunities are the important factor that might prejudice the career choices. A career plan would help students to feel contented in their job, which will directly leads to satisfaction. Preliminary career choice is an intellectual, developmental job that youngsters are projected to have accomplished by the end of their high school year (Super, Savicks, & Super, 1996). Wide range of difference was found when mature students were surveyed, as they were not influenced by the culture but by securing their future. It is also found that in middle class schools career choice counseling was not that important but in affluent schools counseling of making career choices was a norm.

Azizzadeh et al (2003) studies based on medical science students found that career opportunities in combination with prestige are the most important factor in the decision making process of surgical career selection.  Often, it is thought that family and community as a sheer start to workplace willingness; though, this decision plays a key role in launching students on a career path that opens and closes opportunities. Bluestein, Phillips, Jobin-Davis, Finkelberg, & Roarke, 1997 stated in a study that career choices with the influence of future job opportunities are also different in management sciences students and adolescents in the school. Again a research study of Muranguri (2011), confirms the presence of several agents, mixing up to give the resultant of profession selection. According to him, an individual’s own trait, his cultural environment, his family experiences, guidance and expectation provided and pertinent to particular field has an effect on the occupation selected by an individual.

Financial Outcomes

Study conducted on 721 undergraduate students established the role of major sources of financial support for the students; parents and bursaries were found as significant among these.  results also revealed that forecasted future benefits from the career including chances for higher future earnings and promotions were factors that influence career choice amongst undergraduate students in South Africa, besides that visits from professors (for career counseling) is also an influential factors in making career choice.   Another construct or item that is widely discussed in researches is financial constraints faced by individuals, hampering their decision making process of career selection. Kerka (2000) claims that piece of information utilized by individuals and financial resources at their disposal significantly interfere in individuals’ decision making process of career selection. Ushure (2014) lectures indicated that limited finances available to students will affect them negatively in their preferences of profession selection. Their desires to become specialist in renowned fields like engineering, doctorate etc. would be restricted by the availability of finances to them. He also argued that in some cases even the children who belong to low status families’ ends up entering in high status professions despite their high cost. This is because their parents want them to enjoy those aspects of life that they have missed out due to their insufficient wealth.  Bochert (2002), PhD thesis is based on the same topic in which he found that opportunities opened to an individual can contribute significantly in one’s choice of desirable career. Poverty and income constraints hinder their way of career success so these opportunities in various forms help them in shaping their career path.

Ease of subject

Figure 1: source: Factor influencing career choices

Theoretical Framework describes both dependent and independent variables. At the center of the model there exist the dependent variable (choice of career among business students) and the independent variables all around it. Any of these independent variables may affect career choice. These variables and their relations are found from the analysis of the hypothesis developed by this model.

H1: There is no relationship between Financial Outcomes and choice of career of business students.

H2: There is no relationship between Ease of Subject and choice of career of business students.

 H3: There is no relationship between Future Job Opportunities and choice of career of business students.

H4: There is no relationship between Interest in subject and choice of career of business students.

Research Methodology

This is a quantitative research; we have selected questionnaire approach in order to collect primary data from business students. The questions were closed ended. This method will help the researcher to analyze the problem deeply. Business students are the target population for this research. The business students of different business schools of Karachi are selected to answer the questionnaire. Convenient sampling method is used and 145 questionnaires were distributed out of which only 120 were found to be having complete data that can be used for analysis. Response rate was high as 82.75% and only 17.25% of questionnaires were not included in the analysis. Questionnaires consisted of single A4 size page, contained five sections assessing Interest in subject, Ease of subject, financial outcomes, Future job opportunities and career choices. 20% respondents belong to 19-23 groups, 50% belong to 24-27 group, and 30% belong to 28 and above group. The majority were male’s 69% and 31% females. As far as the area of specialization in MBA/BBA is concerned, 22.5% respondents belonged to Marketing; 30% to Banking & Finance, 23.33% to HRM, 4.16% to MIS, 14.16% to Supply and Chain and 5.83% to others.

Data Analysis

The results of Reliability Analysis are discussed below.

Table 1: reliability analysis

The alpha coefficient for the three items of each variables mentioned are as (CC = .517, INT = .689, ES = .533, FO = .751 & FJ = .635). According to standards,the reliability coefficient of .60 or higher is considered “acceptable” in most social science research situations and in this research statistics shows three variables that are independent are acceptable (INT, FO & FJ) and two variables (CC [dependent] & GR independent) are coming close to 0.6.

Statistical Methods

Subsequent suitable statistical tools were used for data investigation.

• Pearson correlation

Allowing to the necessities of the theoretic model, the test of determining the link between variables is Pearson correlation, as it checks the “interdependency” of the variables deliberated in the model.

• 2 . Multiple Regression analysis

To measure the strong point of I.V‟ on D.V and consequence of the model, the multiple regressions are castoff as there are further than one independent variable present in the model and only one dependent variable

Table 2: Correlations

**. Correlation is significant at the 0.01 level (2-tailed)

The correlations table indicated that there exists a high positive association between career choices and interest in the subject (r= .307, n=120, p= .001) and there exists no statistically significant linkage between career choices and ease of subject (r= .077, n=120, p=.406.), financial outcomes (r= .04, n=120, p= .665), and future job opportunities (r= 092, n=120, p= .319). Interest in the subject is statistically significantly associated with none of the independent variables. Ease of subject is statistically significantly associated with future job opportunities (r= .270, n=120, p= .003).Financial outcomes are significantly linked with future job opportunities (r= .483, n=120, p= .000)

Table 3: Model summary

Model summary table indicates suitability of the regression model to the specified data. Backward regression results indicated fourth model to be the most significant one. Fourth model takes in-to account only one independent and one dependent variable. R square value is .094 this means interest in the subject can bring variation in career choices up to 9.4%. . Values of R signal simple correlation that is .307.

Table 4:_ Anova statistics

Focusing on the last model and  interpreting the table, it can be found that interest in the subject can statistically significantly impact the career choices made by business students F (1,117) =12.304, p<0.

  Table 5: Coefficients statistics

With the help of coefficients table, we can conclude the regression equation for the last model and the rationale for rejecting or accepting each null hypothesis:

Hypothesis number 1:

 Backward Regression results taking ease of subject, interest in the subject, financial outcomes and future job opportunities as independent variables and career choices as an dependent variable found that despite the overall significance of- model 1, the three variables in the model are having insignificant values that are ease of the subject (t=1.228, p>0.05), financial outcomes (t=-.320, p> 0.05), future job opportunities (t=-1.194, p>0.05). It excludes the most insignificant of the four independent variables that is finance outcomes (p=.750) in order to improve the model. This makes us accept our first null hypothesis that is, H1: There is no linkage between Financial Outcomes and choice of career.

Hypothesis no 2:

In the second model, we are again left out with three independent variables but the two are again found to be in-significant and the most insignificant of these three that is ease of subject (t=1.220, p=.225) will be excluded. This would again make us accept our second hypothesis that is, H2: There is no linkage between Ease of Subject and choice of career.

Hypothesis number 3:

Despite the exclusion of two variables, the model still needs to be reconsidered since again an independent variable that is future job opportunities is found to be insignificant (t=-1.229 , p=.221). The backward regression test will again exclude this variable in order to improve the model. This will make us accept our third hypothesis that is; there is no linkage between future job opportunities and career outcomes.

Hypothesis no 4:

The last model in the coefficient table incorporating only interest in subject (p=.001) and career choices is found to be the most significant one. Thus our last hypothesis will be accepted that is, H4: there does not exist a statistically important linkage between interest in the subject and career selection.

Regression equation taking interest in the subject as independent variable and career choices as dependent variable is as follows:

CC=.392+ .397INT

Discussion  

The study of earlier research on the career choice of students has highlighted the importance of the career choice decisions and their long term implications on the career of the students. Statistics showed that there is three times higher ratio of dropouts of adolescents from the course/subjects than from university, Higher Education Authority (HEA) evidenced that this is because of wrong initial career choices. Clement H (2014) investigated one of the major factors is the mismatch of personality with a course/career. Lack of fit can be the most dangerous cause of dissatisfaction and end up in to the stress career failure. The empirical studies also revealed that the career choice of the students is influenced by several factors that may vary from country to country due to unique socio-economic, geopolitical and demographic factors.  The study was conducted to test the linkage of career choice with interest in the subject, financial outcome, ease of subject and future job opportunities and job outcome; the earlier research studies have shown a positive association between financial outcome and career choice. Another construct or item that is widely discussed in research is financial constraints faced by individuals, hampering their decision making process of career selection. Kerka (2000) claims that the piece of information utilized by individuals and financial resources at their disposal significantly interferes in individual’s decision making process of career selection. Ushure (2014) lectures indicated that limited finances available to students will affect them negatively in their preferences of profession selection. However, this study’s results revealed weak or insignificant association between the career choice and the financial outcome in the business students of universities in Karachi that could be due to the reason that the respondents participated in the study mainly belonged to private business institutes where the population relatively belong to middle or upper middle class; while the students studying in government universities, who belong to lower middle or lower-lower income group students, are constrained by financial resources and their counselors and parents do consider the financial outcome of the job while advising them on career choice decisions.  

The other factors influencing career choice investigated in this study was future job opportunities and job outcome were found to have weak or insignificant relationship with career choice decisions of the students contrary to the earlier research studies that have shown some linkage between future job opportunities and career choice. It is natural that people always try to forecast or direct sometimes the future which is uncertain, so as students; always try to plan their careers for a secure future where superior job opportunities are the important factor that might prejudice the career choices. A career plan would help students to feel contented in their jobs, which will directly leads to satisfaction.

The factor investigated for its association with the career choice decisions was “ease of subject” that was found to have insignificant relation with the career choice decision in this study. The earlier research studies also did not provide sufficient evidence to establish the relationship of ease of subject with the career choice decision. However a vast majority of candidates enrolled for university majors found themselves lacking intellectual abilities required for certain elective courses. They wanted to ease their lives by avoiding enrollments in such majors or specialized courses. They do not want to involve in the fields that may sound to require extensive hard work (Fizer 2013). 

The fourth and the foremost important factor investigated in the study was the influence of “Interest in Subject on Career Choice”. The empirical studies conducted have determined a positive and significant relationship between interest in the subject and the career choice decision of the student.  Research papers based on respondents from the university of Makere Uganda also depicted that students adopting the agriculture career are mainly interested in this field. About 30.3% respondents agreed on that interest along with their past experience in field lead them to adopt career in agriculture (James and Denis 2015). L.T Wurn et al (2015) literature review on this topic also signaled the same result. Within their review of literature, nurses, pharmacists, doctors and dentists were the basis of attention. The data taken for review comprised of approximately 21 research papers which were published from 2002-2013 in different data bases. The results indicate that health care professionals chosed their career because of personal liking and also to benefit people by their services. The study results also revealed that interest in the subject has some link with the personality type; people have interest in the subject that matches their personality type. Rebecca J. et al (2016) conducted a study on 399 students in Kenya which resulted in that there is a relationship between personality types, and career choice. Most of the students were satisfied with the course they selected before entering the university which indicates that suitable career choice for students would improve satisfaction and success in their course of study and future employment.    

The paper attempted to see the fundamental variable that impacts the choice of career of the business students in relationship with different factors. The study derives that interest in subject has strong and positive relationship while; ease in grades, financial outcomes, and future job opportunities are less related or have minor impact on students’ decision for particular field and subject. Interest in the subject is also related and has some linkage with personality type. Mismatch of the personality and lack of interest in the subject is dangerous, and could end up into disastrous results in terms of student dissatisfaction, demotivation, lack of productivity leading to increased drop outs and career failure. On the contrary, the students’ performance could excel and deliver better results if the area of study is matching and aligned with the intrinsic factors of the individual’s personality, leading to internal satisfaction, motivation and commitment. The previous research revealed that the career choice variables of the students have some association with the financial outcome of the course and future job opportunities, however these variable vary with the socio economic and demographic factors. The career choice of the students is also influenced by the level of their social status, financial resources, affordability and future employability.

Limitations

This study was surveyed with the help of different business institutes; although the students respond well to the questionnaire but still we had 120 responses out of 147 questionnaires floated. The present study is limited in scope of time span and sample size. Particularly, the study focuses only on business students in local universities. Thus, the findings of the study have limited Generalizability.

Recommendations

The above literature analysis and study need further research, the results of which will help us to better comprehend the decision criteria and procedure for students.  As the interest in the subject is found as the dominant factor, which is area of specialization and for this, support is needed from professional instructors and institutes to orient the students on the latest emerging trends, new areas of interest and its impact, to direct the students’ interest in the right direction and make multiple interest areas available to them for making better career choices. It is also important to consider the personality type and intrinsic factors of the student while advising them on their career choice preferences because their future performance and success is directly impacted by these factors, and mismatch of the career choice with the personality could be disastrous.  In view of the importance of the topic and its implications on the future success of the students in their career, it is strongly recommended to make further studies to investigate the factors influencing the career choice of the students; and use the findings in the student counseling and support centers to orient the students about their career choice criteria and options. These research prospects might helpfully be grouped as follows: find new recommendations/implications from prevailing studies; and, incorporate models from other corrections, such as study program features; vary the procedure and time-frame required for particular subject studies.

  (adsbygoogle = window.adsbygoogle || []).push({});

• Alberts, C., Mbalo, N. L. & Ackermann, C. J. (2003). ‘Adolescents’ Perceptions of the Relevance of Domains of Identity Formation: A South African Cross-Cultural Study’ . Journal of Youth and Adolescence , 32 (3), 169-184.
• A, Mc Collum CH ,Miller CC 3 rd ,Holliday KM, Shilstone HC ,  Lucci A Jr ,’Factors influencing career choice among medical students interested in surgery’ . Curr Surg. 2003 Mar-Apr;60(2):210-3
• Alexander, P.M., Holmner, M., Lotriet, H.H. et al. J Sci Educ Technol (2011) 20: 300. doi:10.1007/s10956-010-9254-3
• Bayraktaroğlu, S. (2011). İnsan Kaynakları Yönetimi. 4. Baskı. Sakarya: SakaryaYayıncılık
• Beggs, J.M, J.H. Bantham,and S. Taylor. 2008. ‘Distinguishing the factors influencing college students’ choice of major. College Student Journal . 42(2): 38
• Borchert (2002), ‘career choice factors of high school students’, A Research Paper, Submitted in Partial Fulfillment of the Requirements for the Master of Science Degree with a Major in Career and Technical Education. The Graduate College University of Wisconsin-Stout
• Cavus, Geri and Turgunbayeva; 2015 ‘Factors Affecting the Career Plans of University Students after Graduation’ , International Journal of Humanities and Social Science. 5, No. 5.
• Edwards, K., & Quinter, M. (2012).’ Factors Influencing Students Career Choices among Secondary School students in Kisumu Municipality, Kenya’. Journal of Emerging Trends in Educational Research and Policy Studies , 2 (2): 81-87
• Economic survey of Pakistan, 2016, www.Finance.org.pk / survey / chapter16 / 01 _ growth _ investment.pdf.
• Ferry; 2006, ‘Factors Influencing Career Choices of Adolescents and Young Adults in Rural Pennsylvania’, June 2006, Volume 44, Number 3. Research in Brief, 3RIB7.
• Fizer (2013), ‘Factors Affecting Career Choices of College Students Enrolled in Agriculture’, A Research Paper Presented for the Master of Science in Agriculture and Natural Resources Degree, The University of Tennessee, Martin
• Geçikli, F. (2002). Bireysel Kariyer Planlama ve Geliştirmede İmajın Rolü. İstanbul Üniversitesi İletişim Fakültesi Dergisi, (15), 337-354
• Hewitt, J. (2010). ‘Factors influencing Career choice’. Retrieved on 15/02/2010 from www.ehow.com
• James and Denis (2015) , ‘Factors influencing career choice among undergraduate Students in an African university context: The Case of Agriculture Students at Makerere University, Uganda’ ,j ournal of Dynamics in Agricultural Research 2(2), pp.12-20, May 2015 Article ID: JDAR15/015
• Issa, A.O., & Nwalo, K.I (2008).’ Factors affecting the career choice of undergraduates in Nigerian Library and information science Schools’. African Journal of Library, Archives and Information Science , 18 (1): 23-32
• Kerka, S. (2000). ‘Career choice, gender, race and class’. Eric cleaning house on adult career and vocational education Columbus. ED 421641.
• T Wu RN, BSN, M.M.J. Low, RN, BSN(Honors). K.k. Tan ,RN, BSN, Med,V. LopezRN, MNA, PhD, and S.Y. Liaw, RN, BHSN,MHS(ed), PhD, Y not nursing? ‘A systematic review of factors influencing career choices among health care students’. International nursing review 62,547-562.
• Mashige, K.P., & Oduntan, O.A., (2011). ‘Factors influencing South African optometry students in choosing their career and institution of Learning’. The South African Optometrist , 70(1) 21-28
• Muraguri John Mwangi (2011). ‘Factors influencing degree choices among female undergraduate students at the University of Nairobi – a case study of the 2010/2011 cohort’, unpublished MA research project, University of NairobI
• Olamide, S.O., & Olawaiye, S.O. (2013). ‘The factors determining the choice of career among secondary school students’. The International Journal of Engineering and Science , 2(6):33-44
• Özen, Y. (2011). Kişisel Sorumluluk Bağlamında Kariyer Seçimini Etkileyen Sosyal Psikolojik Faktörler. Eğitim ve İnsani Bilimler Dergisi, 2(3), 81-96.
• Redman, T.&Wilkinson, A. (2001).Contemporary Human Resources Management, NewYork: Financial Times Prentice Hall.
• Tanuja Agarwala, (2008) “Factors influencing career choice of management students in India”,  Career Development International , Vol. 13 Iss: 4, pp.362 – 375
• UNDP: 2014-15, http://hdr.undp.org/sites/all/themes/hdr_theme/country-notes/PAK.pdf
• USHURHE, 2014 – 11 th lecture series: ‘developing a successful career among secondary school teachers in Nigeria: a genetic or environmental factor’
• Waudo, R. (2008). ‘Challenges That Face Women in Managerial Positions in the Print Media in Kenya’, UON: Unpublished MBA project
• Zhang, W. (2007). Why IS: ‘Understanding undergraduate students’ intentions to choose an Information Systems major’. Journsl of Information Systems Education , 18(4), 447-

Conferences

Latest articles, latest coip articles, +general information, publication ethics, indexing and abstracting, international editorial board, open access, lifetime article preservation.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

The Factors that Affect Students’ Decision in Choosing their College Courses

Related Papers

Psychology and Education: A Multidisciplinary Journal

Shar-In A. Sadjail , Darwina J. Sansawi , Mona-Allea L. Matolo , Psychology and Education

Deciding for a college course to take is quiet challenging for a student due to the many influencing factors or reasons in the environment that may affect his own preference. This study aimed to determine the factors that influence the College of Education students at MSU-TCTO in choosing Education as their college course. Further, it tried to determine different approaches in managing the course they are currently taking at the college. The result of this study would benefit the administrators, teachers, parents, students, and future researchers as they gain new insights about the factors that influence the students in choosing their college course. This study made use of a phenomenological-qualitative study conducted to the selected students at the College of Education, MSU-TCTO. Interview guide was utilized to gather the needed data. It consists of guide questions validated by the panel of experts. Data gathered were transcribed, thematically analyzed and interpreted. Based on the findings of the study, the common factors that influence the students in choosing Education as their college course were parental discretion/ influences, external influences, affordability, practicality, personal preference and interest, and socioeconomic problems. Moreover, the respondents' yielded different approaches and ways to manage the courses they are currently taking at the College of Education such as self-determination, overcoming fear of subject difficulties, perseverance and self-confidence, personal preference and interest and developing participation and study habit/skills. In conclusion, there were several factors that influenced the COED students in taking Education course. Further, the course they are currently taking were not solely chosen by themselves. The influence of other people in choosing their course may help or hinder them to succeed in life. Hence, appropriate assistance must be provided to students such as seminar or conference on career guidance, among others to assist them with their course placements and help them invest their full potentials.

liyana hussin

The rapid growth of education in Malaysia nowadays encourage the higher educational institutions to provide the best features of courses which align to national aspirations. The positive competition between public university and private university in the admission of students create the great impact of sustainability of the institutions as well. Thus, this study concentrated to give an overview of the factor that encourage students to choose certain courses in higher educational institutions. The determinants of selection addressed consists of the future career, passion, parental influence and peer influence. As conclusion, this study can help the higher institutions to provide some strategies in increasing the number of admission of students and at the same time improve the academic quality and the governance of institution. Keywords: higher educational institutions, determinant of selections, strategies

Journal of Vocational Education Studies

Muhammad Sayuti

The purpose of the study was to analyze the influence of parental guidance, peer group, school promotion, and career information on course preference decision. This study applied a mixed-method with quantitative (multiple regression) followed by the qualitative stage. The quantitative analysis technique is done by a regression test and continued with qualitative. The results showed that partially and simultaneously, the variables of parental guidance, peer groups, school promotion, and career information affected the decision making in choosing the competency of students of SMK Muhammadiyah in Bantul. Career information factors of 12.98% most influence students&#39; decision making in choosing expertise competencies. School promotion contributed 8.20% of the decision. The peer factor contributes to 4.78% of the students’ choices. The last of these factors was parental guidance, which provided 4.43%.

International Journal of Scientific and Research Publications (IJSRP)

Jerald Moneva

IJAR Indexing

The choice of a course and which school to enter in college is one of the major decisions faced by graduating students which is an important element of their learning process. The course and school selection decision will most likely define their future success. The paper was aimed at assessing the students? course and school preference using a descriptive research design. A survey was administered to respondents totaling 2,231 comprised of grade 12 students enrolled in La Consolacion University Philippines (LCUP) during the second semester of school year 2017-2018. Findings revealed that Accountancy, Education and Hotel and Restaurant Management were the three courses most preferred. However, LCUP was not the top school of choice among respondents. Seven out of ten most preferred courses are current offerings at LCUP, three of which are board courses. The provision of a more intensive internal marketing program is suggested. Likewise, a study on the impact of programs and services and their influence on career and school selection decisions may be conducted to determine which program or service made available to senior high students singly or in combination are determinants of their retention. Follow-up research on the reasons why students choose a course or a school may be also be considered.

Rasa Grigolienė

IMRaD Journal

DR. DAVID C . BUENO , Elaine Rico- Briones

Choosing the right career path is becoming more and more important for young students today. Students have to take into account many things when choosing a career and college major. The main objective of this study was to investigate the factors affecting the choice of school and degree program of first-year students. It utilized the descriptive survey method of research with first-year students from a teacher education college as respondents. The researcher used the structured self-administered questionnaire covering all the variables on a four-point Likert scale that has been modified and adapted from several authorities in the field of Education. Students strongly agreed that it was their personal choice to enroll in their chosen school and degree program. They also strongly agreed that one of the reasons for choosing the school is its wellknown high-quality standards, well-qualified instructors/professors and the school’s ability to become home of board passers. Thus, there was a high level of interest in their chosen degree program.

Assoc. Prof. Dr. Rashad Yazdanifard

On an annual basis, data shows that there are numerous university and college students that fail to complete their courses and graduate with the intended degree or diploma in hand. The aim of this study is to explore the factors that influences the decision making of a student when it comes to choosing varsity courses, investigate how these factors can impact the students’ decisions and provide recommendation on how the cognitive behaviour and academic excellence can be used to make a sound decision, setting the students for success in their varsity life. This research has narrowed down to three major factors which are personal, interpersonal and environmental. The study is conducted using a Likert scale method further expanding the factors into subdivisions for the participants to rank. The population (data set) for this survey is 50 students from all walks of life but concentrated in Klang Valley. The key findings from the study shows the most prominent factor are Personal factors. The second most influential item are Environmental factors. Interpersonal factors come in third. Concluding this research, it is prudent to obtain a bigger and robust data set. A future practical application of this research can be developed by a tool which further provides a systematic solution for all its users to yield the best potential and make the best informed decision when furthering their education.

Evelyn Arondaing

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Procedia - Social and Behavioral Sciences

Norbahiah Misran

Lumen Proceedings

Daniela Stefanescu

Higher Education Policy

Sadi Assaf , Adel Aldosary

Samantha Ferrer

Asari Tharuka

Asian Online Journal Publishing Group

Rachelle Mendoza

Renan Limjuco , Mona Laya

Olusola Ajibola

Naveed Shahzad , Takdees Syed

Farhan Mehboob

Jethro Robles

Indonesian Journal of Multidisciplinary Science

reymark pabilando

Lambert Academic Publishing

Justice Agyei Ampofo

Acta Didactica Napocensia

Simona Sava

Abdijabbar Ismail Nor

Christian Dale Aguda

Asia Pacific …

Siti Hussin

PEDAGOGIA: Jurnal Pendidikan

SCIE Australia

Africa Development and Resources Research Institute ADRRI

Sarerusaenye Ismail

Globus: psychology and pedagogy

Hương Nguyễn Thị Kiều

Loren Agrey , naltan lampadan

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 05 September 2024

A Lucas island model to analyse labour movement choice between cities based on personal characteristics

• Tiange Qi 1 ,
• Yuning Gao 2 &
• Yongjian Huang   ORCID: orcid.org/0009-0001-2870-4216 3  

Humanities and Social Sciences Communications volume  11 , Article number:  1138 ( 2024 ) Cite this article

Metrics details

The labour movement has been a key factor for cities’ development and caused regional inequality between cities. Although empirical studies have been conducted to investigate it, little theoretical evidence has been provided to find out the underlying mechanism. This paper describes and derives a Lucas-Prescott style island model to study the location choices of the heterogeneous agents by utilising endogenous technology growth, which in turn influences personal human capital growth. It leads to the U-shape curve of the inequality of wage income with the technology of these islands but not in terms of total income. In the extended two-goods model, the magnitude of the implications is increased by the impact of non-tradable goods price. Together with empirical research using the US census data, this paper finds that skilled labours with less endowed wealth tend to live in large cities for its high salary. On the other hand, those less-skilled but with more endowed wealth tend to live in cities with better environment, which drives up the price level of non-tradable goods in these cities. This explains the population concentration in the super cities and the high housing price-wage ratio in some beautiful cities, which provides theoretical basement for further empirical studies about labour movement in other cities

Similar content being viewed by others

Trade liberalization, city size, and urban wage premium: evidence from China’s city and individual micro-data

Human capital space: a spatial perspective of the dynamics of people and economic relationships

Demographic and labor force impacts of future immigration flows into Europe: does an immigrant’s region of origin matter?

Introduction and literature review.

Since the advent of the Industrial Revolution, urbanisation has become a common trend across the world and has accelerated a great deal over recent years. According to the World Bank, database Footnote 1 , the rate of worldwide urbanisation has risen from 34% to 37% from 1960 to 2022. This is underpinned by the fact that rates in developing countries such as Mexico have gone from 51% to 81% and in Brazil from 46% to 88% during that period. However, even developed countries such as US and UK have witnessed a rapid increase of the urbanisation rate, going from 79% to 83% and 79% to 84%, respectively within the last 20 years.

The concentration of population is a significant phenomenon for consideration during urbanisation. In specific countries, the population share of largest city to total urban population Footnote 2 is relatively high, such as in the UK (17%), Canada (20%), Mexico (21%), France (20%), Austria (23%), Japan (32%) and Chile (40%) when compared to Germany (5%) and US (7%). However, this percentage has been decreasing since 2000 in many major economies except for Austria and Canada, which have relatively lenient immigration policies. This population concentration brings about inequality, which is seldom addressed. Nord ( 1980 ) finds that with the increasing size of the city population, income equality produces a U-shaped curve, arguing that as the population immigrates to city, the occupational and wage structure of local labour market will change. Small cites lack infrastructure and sufficient size to support the types of business to distribute income. High-income business is monopolized at certain local enterprisers and immigrants have to enter the business with low income. As the city size increases, the improved infrastructure and demand provides capacity to different types of business and they provide higher salary to attract skilled immigrants, which would decrease inequality. However, Madden ( 2000 ) and Glaeser et al. ( 2009 ) point out inequality always increases with population. They find that in 2006, the elasticity of Metropolitan Statistical Area Footnote 3 ’s Gini coefficient to city population is 0.036 and highly significant in US cities by US Census data. Additionally, they find that the mean derives from the top 5% mean income in a Metropolitan Statistical Area in relation to the overall mean income within that Metropolitan Statistical Area is highly significant. What’s more, for high-productivity people, income is disproportionally positively correlated with the size of the city. They think that as population increases within the city, the market expands and the business, which requires high-skilled workers, will increase. This increases the demand for high-skilled workers and their wage return to human capital. At the same time, immigrants from Latin America, which are mainly low-skilled workers, worsens this wage inequality within the city. Wheeler ( 2001 ), Baum-Snow and Pavan ( 2009a ) prove the robustness of this positive effect. The additional premium gained by a greater number of productive people arises in every quintile of the income distribution, as well as the development of the city has a direct effect on inequality even if the effect of socio-economic composition is ignored. Behrens and Robert-Nicoud ( 2014 ) build a model, which links the size of the city to inequality and productivity. In addition to the view that the increase of city size will increase the skill premium, they also argue that urbanization will improve competitiveness and openness to business, which decreases the cost of logistics and transportation and therefore increases the price competition locally (as the price of shipping goods to customers or their nearby shops is low, the local goods’ advantages decrease). This will benefit the entrepreneurs which produce exported goods. And as only most productive companies export their goods, these companies will become richer, which increases inequality and productivity. Higher productivity will also increase the city size by attracting migrates, which is a cyclical effect. Castillo et al. ( 2020 ) notice that labour mobility will bring new knowledge to the companies, which benefits their productivity by knowledge diffusion, regardless of labour’s level of skills.

The core to addressing these facts is understanding the drive behind different kinds of labour movements in different cities. Lucas and Prescott ( 1978 ) argue that decisions concerning labour movement should balance in terms of the trade-off for the benefit of moving to a higher-wage company (as island in the model) and the loss of wages during the period of ‘searching for that company’. Therefore, they conclude that the technology heterogeneity between each island is the main drive behind unemployment as people have incentives to move to companies with higher technology to earn a higher wage, whereas those who are still searching for a better job contribute to unemployment. Then Coen-Pirani ( 2010 ) modifies the island model to include the use of land in the production function and land rent in income, applying it to analyse flow of labour between cities.

The theoretical rationale of this paper utilises the Lucas-Prescott model to address aspects of urbanisation, labour flow, and inequality. Rather than using a homogenised sense of labour in their model, this model introduces skill-heterogeneity between labour based on the model of Behrens and Robert-Nicoud ( 2014 ). Also, the model for this paper replaces land with capital as a complementing factor with labour to produce consumption goods. Additionally, both workers and capitalists are defined as agents. This is because land can be included in capital and it is also not the main factor for production in industrialised countries. In the UK, 57.5% of lands are used for the purpose of agriculture in 2018 (Ministry of Housing, Communities & Local Government, 2020 ), while the percentage of agriculture to UK GDP is only 0.61% (ONS, 2021 ). Introducing capital will add more flexibility and be more realistic. To address the facts of labour flow from large cities to cities that have beautiful natural landscapes, this paper also includes the utility of the quality of the environment, as it affects the movement of labour. Despite the wage mentioned above, another factor affecting job choice is environment (Feld et al., 2022 ). Chen et al. ( 2022 ) also find that the air pollution is responsible for large labour mobility in China: 10% air pollution will result in 2.8% population outflow in China in average. We therefore add the environmental factor in the labour’s utility function.

Our paper makes contributions in several aspects. Firstly, is contributes to the theoretical literatures about labour’s migration choice (Harris, 1970 ; Borjas, 1987 ; Zenou, 2008 ; Coen-Pirani, 2010 ; Chin and Cortes, 2015 ; Zhao, 2020 ; Aksoy and Poutvaara, 2021 ). Previous models address heterogeneous wage levels with homogeneous labours, such as McCall ( 1970 ) Research model where labours have a minimal wage to make them indifferent between accepting the job offer or waiting for a better job, which can also be used to explain unemployment rate. The Hopenhayn ( 1992 ) model addresses the entrance and exit mechanism of business in the labour market, of which the driving force is stochastic productivity of companies. The Aiyagari ( 1994 ) model analyses the matching process of labours with heterogeneous skill levels to homogeneous companies. The Krusell and Smith ( 1998 ) model combines the above two models, which incorporates a model with both heterogeneous labours and companies in terms of productivity. It finds the distribution of heterogeneous companies and their employees. However, these models do not consider the wealth as a factor in the matching process. We argue that the labour choice does not only depend on its human capital but also its asset, which complement their work, as the current research does not notice the negative effect of asset on the intention of labour to move for higher wage. Different from the mostly common search models, we introduce above where labour only care about consumption, our model is the first one to introduce environment to affect the labour choice. These two changes can explain the choices of different types of labours, not only in terms of the heterogeneous productivity but also the resources of the income to different cities.

Secondly, this paper constructs the distribution of labour in terms of workers’ talent and their endowed capital in cities with degrees of different technology and environment, which provides a theoretical support to the empirical work of cities’ wage differences, especially those of developing countries (Gong and Van Soest, 2002 ; Bargain and Kwenda, 2011 ; Buch et al., 2014 ; Cao et al., 2015 ). This is because the technology inequality between cities is server in developing countries and their industry structure means that technology development usually results in pollution, which will lead to higher rate of labour mobility.

Thirdly, this paper explains the price heterogeneity between cities due to the mobility of different labours. Van Nieuwerburgh and Weill ( 2010 ) propose an island model to analyse the effect of labour mobility on the housing price. They argue that the productivity is the determinant of labour mobility and wage level, which affect cities’ housing price, which is consistent with our research. However, the positive correlation between wage and housing price do not apply in some cities, which will be discussed later. Our model with the environmental factor, asset and connection between the tradable goods and non-tradable goods solve this problem. We conclude that even though wage difference due to the technology inequality is positively connected with the price level of non-tradable goods, labour’s mobility choice due to non-wage attributes plays an important role in the price level of non-tradable goods.

The remainder of this paper is organized as follows. Section “The baseline model” discusses the baseline model and establishes the equilibrium condition. And Section “Extended two-goods model” presents a more complicated version of the model, which allows for the inclusion of tradable and non-tradable goods to address the price of heterogeneity between different cities. While Section “Empirical analysis” carries out the simple empirical analysis, to support our model. Last, Section “Conclusion” concludes our paper.

The baseline model

Lucas-prescott model.

Within the Lucas-Prescott economy, people’s income comes from their wages and the rent of capital. At the beginning of the period, each agent is on an island. The shocks are then publicly realised, and people respond to them by deciding to stay or leave the island. If people decide to leave the island, they do not work during the period (but can still receive the interest of the capital) and will go to a new island at the beginning of the next period. If they choose to stay, they can receive both wage income and the rent of capital. The island economy system is a closed economy without any international trade and capital inflow and outflow. Empirically, the system can be seen as a large economy or each island can be seen as a political identity. The monetary policy is neutral and there is no rigidity i.e. it only considers a real term.

A recursive equilibrium with rational and infinite life agents is used in this paper and is a set of functions:

where z is the technology in the island; q is the quality of living in each island or the function of the environment; h is the human capital of each individual; k is the capital owned by each individual and ω ( h ,  k , ϑ| z ,  q ,  K ) is the number of people with human capital h , capital k and the speed of learning ϑ, which is the natural talent of each agent on the island with technology z , capital K and quality q .

μ ( z ,  q ,  ω ,  K )is the density of the island with technology z , quality q and number of people ω and total capital K . w ( ω ,  z ,  μ ,  q ,  K ) is the wage at the island and r ( μ ) is the rate of return to the capital. People can install their capital in each island without any barrier, which suggests that the return of capital for each island is the same. τ w and τ k are the tax rates of wage and capital return.

Each agent cares about both consumption and the quality with utility function. u ( c t , q t ) = ln( c t ) + ln( q t ), wher c t is the consumption at time t . People do not have disutility on labour and each one is endowed with 1 unit of labour in each period.

It satisfies:

\(v(\omega ,z,h,\mu ,q,k,\vartheta ,K)=\,\max \{{v}_{stay}(w,z,h,\mu ,q,k,\vartheta ,K),{v}_{leave}(k,h,\vartheta ,\mu )\}\) , which is the utility of the agent with h , ϑ, k at the island with ω z μ K q . He or she chooses the maximum between the utility of staying at the island or leaving the island at the start of each period.

is the utility of staying at the same island at the period. The agent chooses the investment to decide the capital next period k ’ to maximise the utility of consumption from return of the capital with depreciation rate δ k ∈ (0,1) plus the wage as well as the utility of environment and the expected discounted utility at the next period with discount rate β ∈ (0,1). The notation a’ suggests the variable a at next period.

is the utility of leaving the current island.

The agent is now on the way to search for new island. He or she chooses k ’ to get return from investment while the agent cannot receive the wage at the period and he or she enjoys the environment “on the way” \(\underline{{\rm{q}}}\) , which is lower than the environment of all islands. The agent also decreases the human capital with rate δ h ∈ (0,1).

suggests agent searches for an island to maximise the expected utility and arrives at that island at the start of next period.

z and h follow the growth path \(\dot{z}=\theta (\int h* {e(h,k,\vartheta {|z},q,\omega ,K){dh}\times {dk})}^{\lambda }{z}^{\phi }{z}_{n}^{\gamma }\) , which is the increase of technology and \(\dot{h}=\vartheta {h}^{\eta }{z}^{\kappa }\) is the increase of the human capital.

Where e ( h ,  k ,  ϑ | z ,  q ,  ω ,  K ) is the number of employed people with capital k, human capital h and speed of learning ϑ given the island with technology, quality, the number of people and total capital z ,  q ,  ω ,  K .

The structure of growth suggested by Romer ( 1986 ) will be used, which assumes that technology increase is the function of current technology, researchers in each island and ‘natural technology’ z n , growing constantly at g . It is also assumed that the proportion of human capital used for the research is constant. This is the reason for introducing θ .

It will be applied to human capital growth. Human capital growth is positively correlated to current human capital and the technology of the city. The inclusion of technology is because the human capital need grow faster when the worker works in a company aiming with higher productivity (Abel et al., 2012 ). The elasticity of researchers to technology growth λ , the elasticity of current technology to technology growth ϕ , the elasticity of nature technology to technology growth γ , the elasticity current human capital to human capital growth κ and the elasticity of technology to human capital growth η are constant and within the range of (0,1).

There is a production sector in each island, which hires human capital and capital with production function F(zH, K) , which is homogeneous with degree 1, to solve:

Given w ( ω ,  z ,  μ ,  q ) and r ( μ )

For consistency, each island i with z , q has:

Equilibrium

Taking First order condition wrt K and H for each island i:

The first order condition for capital is used to determine the allocation of capital in each island as ∀ i F 2 ( zH , K ) and is equal for each period.

We can now find the market clearing condition:

For each island with technology and quality z and q , the agents with capital and human capital k and h can choose to

If \({\nu }_{{stay}}(\omega ,z,h,\mu ,q,k,\vartheta ,K)\le {\upsilon }_{{leave}}(k,h,\vartheta ,\mu )\) , people with k and h leave the island and we have ν stay ( ω ,  z ,  h ,  μ ,  q ,  k ,  ϑ ,  K ) = υ leave ( k ,  h ,  ϑ ,  μ ) in equilibrium, people leave the island.

Therefore, we have

If ν stay ( ω ,  z ,  h ,  μ ,  q ,  k ,  ϑ ,  K ) > υ leave ( k ,  h ,  ϑ ,  μ ) and

None of these people come and leave the island. Therefore, we have ω ′( k ′, h ′, ϑ | z ′ ( ω , q , z ), q ′ ( μ ), K ′) = ω ( k , h , ϑ | z , q , K )

If people come to the island and we can pin down ω ′ by the condition

The model also obtains:

which is the density of the island’s next period with technology, quality and distribution of capital and human capital Δ z , Δ q , Δ ω

Steady state

In the traditional Lucas island model, there is a stochastic item in the technology and there is still labour movement in the steady state. However, the distribution in terms of the islands’ technology, production and labour is constant. However, as this model includes more complexity of technology and human capital growth, the existence of stochastic items will never reach a steady state in the model. Recall the growth of z depends on the z last period. If there is a stochastic item in the technology, the growth rate will never become constant. Therefore, for simplicity, our model does not include uncertainty and the steady state can simply be known as the population does change in each island, the technology and human capital grow as a constant rate and quality of environment is also constant. In the real world, it is apparently that it is impossible to reach such a steady state between cities because there are always labour with different levels of talents and wealth entering the market and retiring from the market. However, this model reveals a regulation of labour movement. Empirically, with the personal demography data, regressing their settlement choice with their demography, which is the topic of our future research also with deep empirical analysis, can prove this.

The steps to solving the steady state can be found in Appendix 1 .

The condition for steady state can be solved now:

Condition 1 – For agents with initial value of k 0 , h 0 , ϑ at the time of steady state, they will only be on the island with:

z 0 , q 0 ∈ { z 0 ,  q 0 | v stay ( z 0 ,  q 0 ,  k 0 ,  h 0 ,  ϑ ) ≥  v leave ( k 0 ,  h 0 ,  ϑ )}

Condition 2 – The sum of capital in each island should equal the sum of capital of each agent:

where ξ is the pre-defined unconditional distribution of k 0 and ϑ .

Condition 3 – With the same logic, for each island i, the human capital employed by the company should be equal to the sum of human capital in that island:

Condition 4 – \(\frac{{K}_{t}}{{z}_{t}{H}_{t}}\) is constant which can be solved by (3)

These four conditions suggest that given ξ and μ ( z 0 , q 0 ), there may be many steady state equilibriums if these conditions are satisfied. This is because condition 1 implies that for each pair of ( k 0 , ϑ ), we can always find the set of ( z 0 , q 0 ) of which island the agent can stay in. There can be many allocations of each agent to the corresponding islands if the remaining conditions are satisfied.

Switching from island A to island B increases the utility of quality \(\Delta {u}_{q}=\,\mathrm{ln}(\underline{q})+\frac{\beta \,\mathrm{ln}({q}_{B})}{1-\beta }-\frac{\mathrm{ln}({q}_{A})}{1-\beta }\) , which is constant. In terms of the utility of consumption, switching from one island to another with higher technology will decrease human capital and the level of equilibrium h 0 ( z B , ϑ ) > h 0 ( z A , ϑ ) at the time when the agent arrives at island B. Therefore, the agent needs time to reach the new equilibrium. For the human capital when the agent arrives at the island B, \(\dot{h}=\vartheta {h}^{\eta }{{z}_{B}}^{\kappa }\) , we use the first order Taylor approximation:

Where h * is steady state level of h ( z B , ϑ ) at the current time.

Multiply \(\frac{1}{{g}_{h}h}\) and substitute \(\vartheta {{h}^{* }}^{\eta -1}{{z}_{B}}^{\kappa }={g}_{h}\) :

Where g t is the growth rate of h , at time t .

We define that at the time of arriving at the island 0, the ratio of the agent’s human capital to the equilibrium level of human capital is \(\frac{(1+{g}_{k}){h}_{0}({z}_{B},\vartheta )}{(1+{\delta }_{k}){h}_{0}({z}_{A},\vartheta )}=\pi\) . Then we have:

Therefore, we can solve the time T when \(\frac{{g}_{T}}{{g}_{h}}=1\) if t ≥ T .

As we also know that in steady state 1 + g k = (1 + g h )(1 + g z ) and ln (1 + g z ) can be cancelled out in the consumption utility gain.

The consumption utility gain from switching island Δ u c is:

We are now able to find the locations of agents with different characteristics. We assume that island A is an island with relatively high quality but low technology and that island B is an island with relatively low quality but high technology. Switching from A to B has a positive utility gain from consumption and negative utility gain from quality i.e. Δ u c > 0, Δ u q < 0.

Proposition and their testing

Proposition 1. People with high capital and low speed of learning tend to stay in the island with better environment and people with low capital and high speed of learning tend to stay in the island with higher technology

The above algebra demonstrates that the consumption gain from island A to B is positively related to the speed of learning. As the utility change of consumption is positively related to the change of consumption, the agent who can learn quickly will receive greater benefit from switching island.

It is also noteworthy that the utility consumption is concave in the consumption, implying that the increase in utility related to the growth of consumption from a low level in likelihood will be more than the utility decrease brought about by switching from a high-quality island to a low-quality island, which is irrelevant to the level of wealth. The reason for this is that the initial level of capital does not affect the change of consumption that is caused by switching island while the initial level of consumption does affect the change of utility. In terms of concave utility, given the fixed change of consumption, the higher the initial level of consumption, the lower the change of utility will be.

Therefore, an agent with low capital that can learn rapidly will be more likely to switch to the island with high technology even though they might have to live in an island with the worse environment. Conversely, agents with high capital that learn slowly will gain less from switching to a high-technology island. They are less like to sacrifice the quality of the environment to earn a higher wage.

Proposition 2. People with high capital and high speed of learning or people with low capital and low speed of learning tend to stay in the island with balanced technology and environment

A high (low) level of capital has a negative (positive) effect on the utility gain while a high (low) speed of learning has a positive (negative) effect. Therefore, agents will balance the benefits of wages and quality and are more likely to locate themselves on an island where the level of technology and capital are relatively proportionate.

Proposition 1 could potentially explain the reason that people in the upper-middle economic class ‘escape’ from metropolitans. It could also partially explicate the reason for the high concentration of highly educated people and capital in larger cities. However, Proposition 2 alone is not enough to justify the correlation between inequality and urbanisation. Moreover, it also cannot interpret the reasons for labour concentration only occurring happens in particular countries. This suggests that as there are multiple steady-state equilibriums, different assumptions about the distribution of productive, capital and speed of learning could be added to ‘narrow’ the equilibriums of different countries. Initially, we should assume that the distribution of speed of learning follows a truncated normal distribution in each island from \([\underline{\vartheta },\bar{\vartheta }]\) and has the same mean and variance, which follows the setting of Pluchino et al. ( 2018 ) where the talent of people is randomly assigned. In terms of each level of initial capital owned by the individual, the conditional distribution of the speed of learning to capital follows the same normal distribution as well. We should note that two distributions are independent and it is because the endowment capital is decided by the nature at the time when the city firstly functions in our paper. For example, when migrates find a new continent or they build a new city in a forest, the endowment wealth is the nature resources they have at the time of settling down.

We now start from the economy with two islands A and B and z B > z A , q B < q A , which is consistent with what has already been discussed. It is evident that given ϑ , the net gain from switching A to B decreases with capital k . However, for the given k , we find that the part of Δ u c when t  >  T is:

As we know that \(h\left(z,\vartheta \right)={(\frac{\vartheta {z}^{\kappa }}{{g}_{h}})}^{\frac{1}{1-\eta }}\) , it equals:

which increases with ϑ

Also, as \(\frac{(1+{g}_{h}){h}_{0}({z}_{B},\vartheta )}{(1-{\delta }_{h}){h}_{0}({z}_{A},\vartheta )}=\pi\) , which is irrelevant of ϑ and, T and g t are irrelevant ϑ when t  <  T . Therefore, the degree to which the utility of consumption is different at each time before T , which is the same form after T , increases with ϑ . So we can conclude that with the same k , the willingness to move from A to B increases with ϑ .

Based on the analysis above, we can now produce a graph (Fig. 1 ), which demonstrates visually the location of agents for each k and ϑ .

For given k , Δ u c requires a higher level of ϑ to be equal to the fixed Δ u q . The shape of curve is convex and \(\frac{\partial \Delta {u}_{c}}{\partial k\partial \vartheta } \,>\, 0\) , which implies it needs an increasing marginal value of ϑ for higher level of k to keep the Δ u c constant.

The curve indicates that given the certain value of k , all agents have a speed of learning, of which the gain and loss from switching A to B is equal and below \(\bar{\vartheta }\) , the highest limit of talent, which is not depicted in the graph, but it is assumed that it is above the curve for every k .

As the distribution of ϑ is the same for each k in each island to begin with, we find that the higher the level of k , the fewer agents will be located in the island B, and the higher mean of ϑ for those who are located in the island B. This will lead to income polarisation as all the richest people in island B do not leave whilst the richest people in island A move to island B. This increases the overall number of people that have high wages and capital, and whose income is in the top quartile of income distribution.

With regards to people with lower capital, the lower bound of ϑ to be located in island B decreases, implying that the most impoverish people move to island B. However, as k increases, most people whose ϑ is not high enough will move to island A. These people tend to have an income within the quartile between the richest and poorest, therefore, income inequality becomes more pronounced than in the initial distribution for island B.

The main source of income for people that have a lower capital is their wages and if they do not relocate to the city with higher technological advancement, their consumption will be too low thus they will find it difficult to survive. Therefore, they will often move to chase higher wages even though the level of quality might be lower. The capital income of people who possess a lot capital of is high enough to satisfy their consumption needs; as such they might be more inclined to enjoy the higher quality of life afforded to them by living in a rural and/or seaside city. It is only the richest people whose wages are also high that will opt to stay where they are because they do not want to see a reduction in their income by moving as their wage makes up a significant part of their total income. Furthermore, wage income may not only be a wage given by an employer but can also refer to the income generated by the companies of entrepreneurs which is based in the city where the agent residents.

This could also be the reason that foreign immigrant is seen to gather in metropolitan areas. Migrant workers are generally seen as agents with a low level of capital. The lower bound of ϑ to be in the island B is low, as such most of this group will choose to locate in cities with higher technology. This is consistent with the empirical data gathered in UK where 35% of people who are born outside of the UK live in a capital city (ONS, 2021 ) and is also consistent with Gordon and Kaplanis’ ( 2014 ) research which argues that immigrants are the reason for the increase of low paid jobs in metropolitans.

The multiple islands economy will now be examined. No matter what the distribution of the ( z , q ) of the islands is, one island must be the best in terms of technology and the other in terms of equality. We will call them island B and A for consistence.

For υ leave ( k , ϑ ) in the steady state of every level of k and ϑ , it searches for islands with z , q to maximise

When desiring to move, agents will be able to find their corresponding ideal island and if we draw all the ( z , q ) sets on the 2-dimensional graph, the necessary condition for ideal islands is that they must lie on the northeast frontier (connecting all of the points, which has no points on the northeast side in Fig. 2 ). In terms of Island E and F, they cannot be ideal islands as there are islands with both a higher z and q .

The graph describes the islands with different environments and technologies. The ideal islands should be the ones on the frontier.

When comparing B and D, the lower bound of ϑ B ( k ) can be found above which the agent prefers B to D and below which the agent prefers B to D. Then we compare D and C and trace the lower bound of ϑ D ( k ) above which the agent prefers C to D and below which the agent prefers C to D. Again, going through the same steps, we can outline ϑ C ( k ) above which the agent prefers C to A and below which the agent prefers C to A. Therefore, as the agents are rational, which implies the transitivity of their preference, the ideal island of the agent with ϑ and k in the region [ ϑ x ( k ), ϑ y ( k )] is y . Moreover, as we know that the lower bound ϑ y ( k ) decreases with k , it is possible to draw the graph (Fig. 3 ) describing the ideal location of agents with different ϑ and k:

The areas in the graph are the ideal islands to settle down for the individuals with corresponding knowledge and wealth.

Agents that will move are now the focus of the research. As has been established, on the frontier, a higher quality island must be accompanied with lower levels technology. For each level of k and ϑ , the conclusion of the two islands economy for each two neighbour islands can be applied to find the ideal island for each agent. The agent with highest level of k and the island with highest level and second highest level of z is the starting point. For simplicity, we continue to use the six islands case (it can be applied to any number of islands with the only difference being the times of the comparison).

Given k , as an agent’s choice of an ideal island is determined solely by the speed at which the agent learns, their income is determined by it as well. This means that the analysis of the income distribution can be simplified to the speed of learning distribution.

However, for each region of y on each island, not all agents will move to their ideal island as the cost of moving and living there might be too high for them to afford i.e. Δ u c  + Δ u q  < 0. For each island i, the threshold value of \({\vartheta }_{y}^{i}\) can be calculated, where the agent is indifferent about whether they will move to island y or not ( y ≠ i ).

In other words, the Fig. 3 is different from Fig. 1 , as Fig. 3 indicates the island where the agent wants to move if the agent is forced to be 'on the way' to search for an island for one period and arrive to a island at the next period. This island is called 'ideal' island. Therefore, there are agents who want to stay at the current islands if they are not forced to move. \({\vartheta }_{y}^{i}\) is the threshold of talents of people in island i who are indifferent between moving to island y. \({\vartheta }_{y}^{i}(k)\) is actually the ϑ ( k ) of Fig. 1 .

For the neighbouring island i, \({z}_{i}\, > \,{z}_{y}= \,>\, {\vartheta }_{y}^{i} \,<\, {\vartheta }_{y}\) and \({z}_{i}\, < \,{z}_{y}= \,>\, {\vartheta }_{y}^{i} \,>\, {\vartheta }_{y}\) .

This is because in terms of the original island and the target island, the absolute value of Δ u c is positively correlated with ϑ . Moreover, moving to other islands incurs additional costs of utility; moving to islands with higher technology requires higher ϑ to gain more income and offset the costs. With the same logic, moving to a lower technology island will require lower ϑ to experience a smaller decrease in income. We also assume that \({\vartheta }_{y}^{i}\) is within the region y to ensure that there are agents moving to all other islands.

All the people in region B stay in island B and some of people from region B in other islands move to island B. Furthermore, there is an outflow of people from island B to other regions, which increases the proportion of region B people who are the richest on island B. With the same logic, island A faces a concentration of the poorest people as well as an outflow of people in other regions. In both island A and B, inequality is increased compared to their original states.

However, regarding island C and D, we see an outflow of both the richest and poorest people and inflow of middle-class people, which causes inequality to decrease.

The unattractive islands like E and F face a population outflow and only those who suffer a loss of utility because of bad quality and the loss of wages during moving which offsets the gain of moving to the ideal island will stay. The percentage of the population that stays is non-decreasing with z and q given the distribution of ( k , ϑ ) fixed.

The conclusion we can draw from that is that, for given k, wage inequality decreases then increases as the technology of the island also advances. Therefore, as the wage distribution starts out as the same (truncated normal), the total wage inequality also follows the same trend as the one conditional on k .

As k decreases, the area of region B increases, and the concentration of region B agents moving from other islands to island B also goes up. This leads to an ambiguous effect on inequality as it did for the high-technology island. For each k (i) the average income decreases as k decreases but (ii) the proportion of agents with capital k to the total number of agents in that island increases. It is impossible to know which effect dominant, therefore we also cannot know why the income distribution has shifted. The effects are the opposite for island A compared to those of B as k increases, but still the two effects conflict with each other.

Extended two-goods model

Description.

The difference compared to the baseline model is that we now have two consumption goods:

Tradable goods, which can be traded between islands, must have the same price for all islands, as such we can normalise the price as 1. They will be referred to as good A.

The non-tradable goods, which can only be consumed locally, will have different prices depending on the island. They will be called good B.

The utility of the agent now is

We now have Bellman equation for the agent:

The modification is that an agent can immediately arrive at any of the other islands without delay but can only start working during the next period.

z A and h follow the growth path \(\dot{{z}_{A}}=\theta {({H}_{A})}^{\lambda }{{z}_{A}}^{\phi }{{z}_{n}}^{\gamma }\) and \(\dot{h}=\vartheta {h}^{\eta }{{z}_{A}}^{\kappa }\) and H A is the human capital working to produce A. Following the deviation performed in part I, we have in steady state:

The technology of the non-tradable good z B is the same for all islands as it grows at the constant rate: \({g}_{{\rm{zB}}}={g}_{z}=\frac{1-\eta }{\left(1-\phi \right)\left(1-\eta \right)-\lambda \kappa }g\) , i.e. all technologies grow at the same rate in steady state.

There are two production sectors in each island for two goods. The sector of good A is the same, which is homogeneous with degree 1, to solve:

Given w ( ω , z A , μ , q ) and r ( μ )

The sector of good B, with its price p B ( ω , z A , μ , q ) given, is also homogeneous with degree 1 and it solves:

For consistence, in each island: \({H}_{A}+{H}_{B}=\int h* e(k,h,\vartheta |{z}_{A},q,\omega ){dkdhd}\vartheta\) and K A + K B = K

We solve the first conditions for two sectors and apply the law of one price on Appendix 2 .

We now can solve other conditions for steady state:

Condition 1 – Agents with initial value of k 0 , h 0 , ϑ at the time of steady state, will only be on the island with

Condition 2 – The sum of capital for each island should equal the sum of capital for each agent:

Condition 4 – At each island the total production of non-tradable goods is equal to their total consumption:

Similarly to part I, any distribution of ω and μ as well as satisfying the above conditions can suffice as the equilibrium, as such there are multiple equilibriums.

Compared with part I, the locations of labour based on the characteristics and the differing distributions of islands are comparable while people’s willingness to move to the higher quality cities is determined by the lower prices-wage ratio of their non-tradable goods. The region of area B and the concentration to island B both perform a decrease. Therefore, with regard inequality, island B as a lower rate and island A has a higher rate when compared to part I.

Adding the non-tradable good improves the previous model by imposing stricter conditions for equilibrium. This suggests that in the islands with higher technology, the non-tradable goods have higher price even though these goods are the same for all islands. The intuition is that higher technology islands have higher wage for the tradable goods. To ensure that people willing to work for the non-tradable sector, two sectors must have the same wage. To cover the additional cost from the wage, the non-tradable sectors must increase the price. This is the Balassa–Samuelson effect (Samuelson, 1964 ) and Kravis and Lipsey ( 1983 ), which is found statistically significant by Samuelson ( 1994 ). Vaona ( 2011 ) and Nenna ( 2001 ) provide evidence supporting it from intra-country level in Italy, and Songtao ( 2009 ) tests it for real estate’s price between cities in China, indicating that the cities’ higher technology level is positively correlated with the wage and price of real estate sector, which is the rationale of applying Balassa–Samuelson effect on the city-level.

The adjusted model used in this section leads to a similar distribution when contrasted with that of part I. However, the non-tradable goods provide another intuition to explain labour mobility and its causes. The labours also consider the price of non-tradable goods as a factor determining the decisions of labour mobility. When considering where to work, people do not only consider the wage but also the purchase power of it on local food, service, or accommodation. For the labours with higher talent but lower wealth, they mainly rely on wage income. As we discussed in the Appendix 2 , the wage in large cities is more affordable for non-tradable goods. Comparing to proposition 1 of part 2, they are more likely to move to the cities with higher technology. Conversely, for labours with lower talent but higher wealth who mainly rely on capital income, they are more likely to move to the cities with better environment because the price level is higher in large cities. For people with low talent and low wealth or high talent and high wealth, the conclusion of proposition 2 of part 2 is the similar as they also balance between the gain of more affordable wage income and less affordable capital income from moving to a higher technology city.

It also accounts for the low wage-price ratio in cities with superior environments. This is consistent with the positive correlation between the natural environment and house prices in previous literature. Luttik ( 2000 ) and Donovan et al. ( 2019 ) estimate the effect of demographic factors on house prices and reveal the significant positive correlation between trees, water, open spaces, and house prices. Furthermore, Catte et al. ( 2004 ) and Steegmans and Hassink ( 2017 ) find that wealth of the buyers has a significantly positive effect on housing prices. Generally, housing prices are also positively correlated to the level of local wages. However, there are also cities with superior environment where local wages are relatively low also with high housing prices. Therefore, we might assume that areas with higher house prices are more likely to be those with higher-income residents whose wealth drives up house prices. This implies that cities with relatively low technology but a relatively high quality of environment might also attract households with high incomes. Achieving a high income in the city with low wages is brought about through these households that have capital income from other cities. This implies that if a city government spends too much on developing tourism while ignoring the investment on R&D, the population in that city will be people with high capital store and low skill, which drive up the price level but do not help with the wage increase. A typical city in China is Sanya, which is famous for its natural environment. Its house price-wage ratio is of top 5 in China while its average wage is even not in top 50 (Chinese National Bureau of Statistic, 2022 ).

Empirical analysis

We apply the empirical analysis to support the Lucas-Prescott style island model. The empirical analysis is based on the American population movement data in IPUMS USA from 2016–2021 at state level. And we use the patent data from US patent and trademark office to proxy for the technology level, and the higher number of patents indicates the high level of state technology level. The air quality index (AQI), the comprehensive evaluation of pollutants such as SO2, NO2, PM10, PM2.5, O3 and CO, from US Environmental Protection Agency to measure the environmental level. While, the bigger AQI implies the worse state environment.

To test the impact of individual wealth and education on the living choice, we construct the following Logit model:

and Probit regression model:

where i represents citizen, j represents state and t represents year. Environmental takes value of 1 if individual i lives in a state j with good environment quality and 0 otherwise, by the median of AQI. And Tech nology takes value of 1 if individual i lives in a state j with high technology and 0 otherwise, by the median of patent number. Φ is the cumulative distribution function of the standard normal distribution. Education level (Education) is the proxy variable for individual skill, measured as the sequence from 1 to 11 representing the lowest education level to the highest level and the value of home with logarithm is used to proxy for individual wealth. Controls is a vector of individual control variables include sex, age, logarithm of one plus income (including rental income from housing) and owning farm or not. And the descriptive statistics of all variables are reported in Appendix 3 . To control unobservable factors potentially affecting living choice, we further control the year, work industry and county fixed effects, with standard error clustered at family level.

Following Taiwo ( 2013 ), the results are presented Table 1 , with Columns (1)–(4) reporting Logit model and Columns (5)–(8) reporting Probit model. And columns (1), (3), (5) and (7) report the result of odds ratio, with others reporting the coefficients. The Columns (1)-(2) and (5)-(6) show that the probability of citizens living in states with poor environment increases with their skill level and decreases with their wealth. And Columns (3)-(4) and (7)-(8) reveal that those owning little wealth with high skill level have higher chance to live in state with relatively high technology level. In conclusion, citizens with high skill-level and little wealth tend to choose the state with poor environment and high technology level, and these with low skill-level and huge wealth prefer to live in state with good environment and low technology level. This is consistent with the pervious theoretical analysis that, as higher technology of the location and education level of the individual bring higher wage level, those with little endowment wealth but higher education level have to work in places with higher technology but worse environment to enjoy higher wage to cover their expense. On the contrast, labours with higher wealth tend to live in a place with better environment as they can benefit from capital rent wherever they live and they can gain more utility from the environment. However, we only regress the labour choice at the state level. The further research will be conducted to test if the theory still holds at the county/city level, which is more precise and convincible.

This paper describes and derives a Lucas-Prescott style island model, to study the choices of heterogeneous agents’ decision about where they want to live. Differing from other islands model which assume the implementation of the stochastic technology process, it utilises endogenous technology growth, which in turn influences personal human capital growth. This leads to the balanced growth equilibrium in steady state, which is potentially the first model that combines balanced growth and the Lucas-Prescott model. Compared to the conventional Lucas-Prescott models where only the distribution is fixed in steady state, this model provides the fixed location of each agent, which admittedly makes it less flexible. However, that is compensated by less restrictive steady state conditions, which leads to multiple steady states.

The baseline model’s steady state indicates that the islands (cities) where people ideally want to stay in must be the islands on the frontiers (see Fig. 2 ). Among these islands, agents that have a higher speed of learning and lower capital tend to reside in urban metropolitans, while people who rely mainly on non-wage-based income prefer to live in a city with better environment. It leads to the U-shape curve of the wage income inequality in terms of the technology of these islands, which is consistent with Nord’s ( 1980 ) research. However, in terms of total income, the inequality is ambiguous. The two-goods model includes both tradable and non-tradable goods, which interprets the high price-wage ratio of the cities with good environments through the Balassa–Samuelson effect. Even though the implications behind the choice of a location still holds, its magnitude is increased by the impact the price of non-tradable goods.

However, this paper does not consider the role of the government to allocate resources to attract workers, support technology development, and improve the environment. It also lacks the deep analysis of the real-world data. To complete the research, the future study will add the government to the model and suggest different policies for the government with different goals. Then we will simulate the model, which is calibrated based on the data of US. Also, we will evaluate the model based on microdata of individual labour at the county/city level. For example, apart from the labour choice, which is mentioned before, macro-level data of each city’s price level will be regressed to examine the fit of the simultaneous effects of migration and the price of non-tradable goods on each other to the model.

Data availability

The data generated during and/or analysed during the current study are available in the supplementary files.

The level of urbanization is calculated as the proportion of citizens in the urban area to the total population within the country. The data is accessible from World Bank database at: https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS

The data is accessible from World Bank database at: https://data.worldbank.org/indicator/EN.URB.LCTY.UR.ZS

Metropolitan Statistical Areas are core based statistical areas (CBSAs) associated with at least one Urban Area that has a population of at least 50,000. The metropolitan statistical area comprises the central county or counties or equivalent entities containing the core, plus adjacent outlying counties having a high degree of social and economic integration with the central county or counties as measured through commuting, according to the definition of US Census at: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7 .

Abel JR, Dey I, Gabe TM (2012) Productivity and the density of human capital. J Reg Sci 52(4):562–586

Article   Google Scholar  

Aiyagari SR (1994) Uninsured idiosyncratic risk and aggregate saving. Q J Econ 109(3):659–684

Aksoy CG, Poutvaara P (2021) Refugees’ and irregular migrants’ self-selection into Europe. J Dev Econ 152:102681

Bargain O, Kwenda P (2011) Earnings structures, informal employment, and self‐employment: new evidence from Brazil, Mexico, and South Africa. Rev Income Wealth 57:S100–S122

Baum-Snow, N and Pavan R (2009a) Inequality and city size. Brown University, US, Rhode Island

Behrens K, Robert‐Nicoud F (2014) Survival of the fittest in cities: Urbanisation and inequality. Econ J 124(581):1371–1400

Borjas GJ (1987) Self-selection and the earnings of immigrants. Am Econ Rev 77(4):531–553

Google Scholar  

Buch T, Hamann S, Niebuhr A, Rossen A (2014) Attractiveness of regions and cities for talents and entrepreneurs. Econ Geogr 90(Supplement S1):59–77

Cao G, Li M, Ma Y, Tao R (2015) Self-employment and intention of permanent urban settlement: evidence from a survey of migrants in China’s four major urbanising areas. Urban Stud 52(4):639–664

Catte, P, Girouard, N, Price, R, & André, C (2004). Housing markets, wealth and the business cycle

Castillo V, Figal Garone L, Maffioli A, Rojo S, Stucchi R (2020) Knowledge spillovers through labour mobility: an employer–employee analysis. J Dev Stud 56(3):469–488

Chen S, Oliva P (2022) The effect of air pollution on migration: evidence from China. China. J Dev Econ 156:102833

Chin, A, & Cortes, KE (2015). The refugee/asylum seeker. In: Handbook of the economics of international migration. 585–658. North-Holland, Elsevier

Chinese National Bureau of Statistic. (2022). Statistical Yearbook, various issues

Coen-Pirani D (2010) Understanding gross worker flows across US states. J Monet Econ 57(7):769–784

Donovan GH, Landry S, Winter C (2019) Urban trees, house price, and redevelopment pressure in Tampa, Florida. Urban For Urban Green 38:330–336

Feld B, Nagy A, Osman A (2022) What do jobseekers want? Comparing methods to estimate reservation wages and the value of job attributes. J Dev Econ 159:102978

Glaeser EL, Resseger M, Tobio K (2009) Inequality in cities. J Reg Sci 49:617–646

Gordon IR, Kaplanis I (2014) Accounting for big‐city growth in low‐paid occupations: immigration and/or service‐class consumption. Econ Geogr 90(1):67–90

Gong X, Van Soest A (2002) Wage differentials and mobility in the urban labour market: a panel data analysis for Mexico. Labour Econ 9(4):513–529

Harris JR (1970) Migration, unemployment and development: a two-sector analysis. Am Econ Rev 60(1):126

Hopenhayn HA (1992) Entry, exit, and firm dynamics in long run equilibrium. Econom: J Econom Soc 60(5):1127–1150

Kravis IB, Lipsey RE (1983). Toward an explanation of national price levels, vol 52. International Finance Section, Department of Economics, Princeton University

Krusell P, Smith Jr AA (1998) Income and wealth heterogeneity in the macroeconomy. J Polit Economy 106(5):867–896

Lucas Jr, RE, & Prescott, EC (1978). Equilibrium search and unemployment. In Uncertainty in economics. Academic Press, UK, Cambridge, Massachusetts, pp 515–540

Luttik J (2000) The value of trees, water and open space as reflected by house prices in the Netherlands. Landsc Urban Plan 48(3-4):161–167

Madden, JF (2000) Changes in Income Inequality within U.S. Metropolitan Areas. Kalamazoo, MI: W.E. Upjohn Institute for Employment Research

Ministry of Housing, Communities & Local Government. (2020). Land Use in England, 2018

McCall JJ (1970) Economics of information and job search. Q J Econ 84(1):113–126

Nenna, M (2001). Price level convergence among Italian cities: any role for the Harrod-Balassa-Samuelson hypothesis? Universita degli studi di Roma” La Sapienza”

Nord S (1980) An empirical analysis of income inequality and city size. South Econ J 46(3):863–872

Office for National Statistics. (2021). House price to residence-based earnings ratio. https://www.ons.gov.uk/peoplepopulationandcommunity/housing/datasets/ratioofhousepricetoresidencebasedearningslowerquartileandmedian

Pluchino, A, Biondo, A, Rapisarda, A (2018). Talent vs Luck: the role of randomness in success and failure

Romer PM (1986) Increasing returns and long-run growth. J Polit Economy 94:1002–1037

Samuelson, PA (1964). Theoretical notes on trade problems. The review of economics and statistics, 145-154

Samuelson PA (1994) Facets of Balassa‐Samuelson thirty years later. Rev Int Econ 2(3):201–226

Songtao, W (2009). Urban Openness and Real Estate Prices: Empirical Evi-dence from Thirty—Five Large Scale Chinese Cities [J]. Nankai Economic Studies, 2

Steegmans J, Hassink W (2017) Financial position and house price determination: an empirical study of income and wealth effects. J Hous Econ 36:8–24

Taiwo O (2013) Employment choice and mobility in multi‐sector labour markets: theoretical model and evidence from Ghana. Int Labour Rev 152(3-4):469–492

Van Nieuwerburgh S, Weill PO (2010) Why has house price dispersion gone up? Rev Econ Stud 77(4):1567–1606

Vaona A (2011) Intra-national purchasing power parity and Balassa–Samuelson effects in Italy. Spatial Econ Anal 6(3):291–309

Wheeler CH (2001) Search, sorting, and urban agglomeration. J Labor Econ 19:879–899

Zenou Y (2008) Job search and mobility in developing countries. Theory and policy implications. J Dev Econ 86(2):336–355

Zhao X(2020) Land and labor allocation under communal tenure: theory and evidence from China. J Dev Econ 147:102526

Download references

Author information

Authors and affiliations.

Department of Economics, University of Cambridge, Cambridge, UK

School of Public Policy and Management, Tsinghua University, Beijing, China

School of Finance, Central University of Finance and Economics, Beijing, China

Yongjian Huang

You can also search for this author in PubMed   Google Scholar

Contributions

Author contributions are as follows: Conceptualization, T.Q.; methodology, T.Q.; software, Y.H.; validation, Y.G.; formal analysis, TQ. and Y.H.; investigation, Y.G.; resources, Y.H.; data curation, Y.H.; writing—original draft preparation, T.Q., Y.H. and Y.G.; writing—review and editing, Y.H; visualization, Y.H.; supervision, T.Q. and Y.G.

Corresponding author

Correspondence to Yongjian Huang .

Ethics declarations

Competing interests.

The authors declare no competing interests. None of the authors on the paper are members of this journal’s editorial board.

Ethical approval

Ethical approval was not required as the study did not involve human participants. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Qi, T., Gao, Y. & Huang, Y. A Lucas island model to analyse labour movement choice between cities based on personal characteristics. Humanit Soc Sci Commun 11 , 1138 (2024). https://doi.org/10.1057/s41599-024-03627-9

Download citation

Received : 06 November 2023

Accepted : 19 August 2024

Published : 05 September 2024

DOI : https://doi.org/10.1057/s41599-024-03627-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies