Blue blood students of occupational dynasties and their university choice: the moderating role of parent–child occupational following

ABSTRACT

This study sheds light on a new category of students in higher education marketing literature – namely, occupational followers. We draw on expectancy-value theory, occupational following theory, and consumer buying behaviour premises to develop and empirically test a model for university choice decision. Data was collected from 367 prospective university students divided into two groups: occupational followers and non-occupational followers. Data was analysed using structural equation modelling. Results indicate that staff–new student online interactions are a key driver of new student satisfaction with online subject taster programs. The study provides empirical support for the direct effect of student satisfaction in shaping students’ extra-role behaviour and university brand preference. Specifically, occupational following moderates the relationship between brand preference and university decision as well as the relationship between extra-role behaviour and university decision.

KEYWORDS:

• Occupational following
• university decision
• university brand preference
• extra-role behaviour
• staff–student interaction
• student satisfaction

Introduction

University choice decision is a unique decision which often predicts the trajectory of a student’s adult life. Many human decisions in social science are premised on the concept of reciprocity and exchange (Homans, 1974), likely including the university choice decision. University students invest significant costs in terms of time, effort, and money in exchange for a set complex and unpredictable opportunities in the short and long term (e.g. employment after graduation). Although the opportunities promised by university education are great, they are by no means set in stone (Canterbury, 2000). This uncertainty set the stage for previous works to analogize the action of making the university choice decision as taking a leap of faith (Elsharnouby, 2016). Conventional knowledge suggests that students who follow in their parents’ occupational footsteps, known as occupational followers (Laband & Lentz, 1983), are not on equal footing in terms of costs incurred and benefits realized as a result of their university decision due to the resources their parents extend to them. Follower students may join their family business after graduation and, in the normative event of their parents’ death, inherit the business. Other followers count on their parents’ professional networks for employment after graduation. Followers may also be more familiar with their study program due to their life exposures to their parents’ professions and/or can count on their parents for academic support due to their parents holding similar university degrees. In exchange terms, these students may have to make less effort to succeed and have more benefits than non-followers. Therefore, the leap of faith analogy may be appropriate to describe non-follower students whereas followers may be more accurately described as heirs of occupational dynasties in professional training.

Despite being a widespread phenomenon, parent–child occupational following has not received much attention from higher education marketing researchers. We found no studies specifically focusing on occupational followers within higher education marketing; volumes on occupational following exist in sociology and economics, yet the Western marketing-related landscape is barren. In societies characterized by a rigid social hierarchy, an individual’s success is based on not only skill and effort but also class birth. Social advancement is therefore influenced and/or governed by unfair conditions, such as knowing the right people. This class barrier, along with others, increases the challenges an individual faces for upward movement within the social strata. Such inequality is similar to the behaviour some parents of occupational followers exhibit when utilizing their professional networks to augment their children’s career prospects (Kong, 2017). The occupational following phenomena might have been intentionally overlooked in marketing literature as universities are considered a universal vehicle of social mobility. Social mobility refers to the possibility for anyone with the ability and motivation to succeed, thereby offsetting the role of social class in determining economic outcomes (Haveman & Smeeding, 2006). Universities have long been regarded as a pillar of modern society. Equal opportunity is considered a cherished value in egalitarian societies (Burgess & Steenkamp, 2006). Therefore, universities in the West may have moved away from developing special recruitment efforts for occupational followers due to its possible social injustice dimension. Accordingly, a clear gap exists in the literature which this current study aims to fill.

Drawing on the occupational following theory (Laband & Lentz, 1983, 1985), expectancy-value theory (Eccles et al., 1983), and dominant theorized relationships in consumer buying behaviour, this paper compares followers and non-followers by examining the impact of staff–new student online interactions on student satisfaction as well as the effect the latter has on extra-role behaviour and university brand preference. These two constructs’ effect on university choice decision is then examined. We also investigate the moderating effect of occupational following on the antecedents of university decision.

With this objective in mind, we identified an online direct experience program (i.e. subject-specific tasters) commonly offered to new students during the pre-enrolment stage–onboarding stage. We captured student responses immediately after participating in the online tasters. Collected student responses were stored and analysed against financial data, which are an actual representation of payment/non-payment of tuition fees after the commencement of the following semester.

Theoretical framework and literature review

Expectancy-value theory

The expectancy-value theory has four main tenets: alignment with self-concept, intrinsic value, utilitarian value, and relative cost (Eccles et al., 1983). Within an educational context, alignment with self-concept occurs in a student’s identification with the university program of study (e.g. ‘I have always wanted to be a lawyer’). Intrinsic value is similar to the sense of enjoyment performing the task (e.g. some students with math skills enjoy solving mathematical problems). Utilitarian value refers to students expecting to be rewarded after graduation, such as with enhanced social status and premium salaries. Finally, relative cost refers to students finding the time, money, and effort of success to be acceptable compared to expected benefits (e.g. premium earnings after graduation). Relevant to the theory is the nature of motivation to complete a task which is either extrinsic or intrinsic in nature (Ryan & Deci, 2000). Innate desires are interstice motives, such as students studying a subject because they enjoy it, whereas extrinsic desires include ways to avoid negative consequences, such as dropping out of university or ceasing disruptive class behaviour to avoid loss of social status.

Occupational following

Occupational following theory defines individuals (i.e. students) following in their parents’ occupational footsteps (Laband & Lentz, 1983) as followers. The centrality of university program choice and occupational following is evident in the literature. Follower students of accountants, lawyers, physiatrists, and architects are twice as likely to choose their parents’ occupations as non-follower students according to a study conducted in an Italian context (Aina & Nicoletti, 2014). In another study in the Italian context, the probability of follower students to choose a pharmacy program was 18% compared to 1.3% for non-follower students (Mocetti, 2016). In a study conducted in the United States, follower students were found to be 3.7 times more likely to choose engineering than non-follower students (Godwin et al., 2014).

Parents influence their children’s career path decisions through two main interrelated mechanisms. First, proponents of the gene theory argue that parents transmit their genes to children, affecting a wide range of innate abilities (Mocetti, 2016). Some of these abilities may enable children to develop personal characteristics valued in their parents’ occupational fields. Many examples exist in science, music, and athletics (e.g. Thomas and Theodore Edison; Bob and Ziggy Marley). Another explanation for children’s development of personal characteristics may be the 10,000-hour rule, which states that 10,000 h of learning and practice are needed to obtain a high level of expertise (Gladwell, 2008). Children may have spent many hours learning from their parents, through either formal or informal processes. Parents transfer human capital and job-specific knowledge to their children (Dunn & Holtz-Eakin, 2000), and their life interactions with their children are a significant factor shaping their children’s self-concept (Desforges & Abouchaar, 2003). Parents may more directly transfer specific job-related skills by providing their children with on-the-job training to better equip them for occupational following (Aina & Nicoletti, 2014). The manifestation of the 10,000-hour rule may be evident in Trenor et al.’s (2008) study, in which some follower engineering students reported liking certain science-related courses during their early school years. Second, parents transfer resources to follower students through monetary inheritances, as heirs to family name brands (Laband & Lentz, 1983), and/or through their occupational networks (Kong, 2017). Follower students reportedly earn premium incomes more often than their colleagues (Laband & Lentz, 1985). Parents also give their children access to professional networks, thereby putting follower students in touch with powerful persons in control of valuable resources (Kong, 2017). Indeed, one study reported that 40% of Canadian men were at some point in their careers employed at the same company/employer where their parents had worked (Corak & Piraino, 2011). More recently, occupational following behaviour was reported in Greece during Covid-19 lockdowns, when parents encouraged their children to find financial security by joining the family business (Christoforou et al., 2021).

University choice decisions

We define university choice decision as a student’s decision to attend one college/university over another. Students normally understand the significance of the university decision as one of the most important decisions in their lives. The literature cites similarities between students and consumers, including paying fees, shopping around, and complaining behaviour in the case of delivery failure (Eldegwy et al., 2022b). Like consumers, students go through the classical buying behaviour characteristic of the high involvement service decision-making process described in consumer buying behaviour literature. These three stages are the predisposition stage, in which students are influenced by financial and social considerations triggering the aspiration (or not) to advance to higher education; the search stage, in which students investigate their fit with institutions according to their academic eligibility and overall financial considerations and develop a short list of universities; and the choice selection stage, in which students – after careful evaluation – select their preferred university from the short list (Murphy, 1981). Research indicates that the preferred university is not necessarily the best university, but rather where the student finds the right institution fit (Banning & Banning, 1986). However, education is fraught by double intangibility, making it difficult to evaluate, as it cannot be touched nor, due to its complexity, fully mentally grasped (Edvardsson et al., 2005). Previous works consistently draw parallels between students’ and consumers’ buying behaviour of relying on clues of quality when evaluating intangible services dominated by experience and credence (Eldegwy et al., 2018). Students usually seek direct experiences with the service provider/university (e.g. attending open days) to conceptualize the benefits expected from education and collect information about the cost of a university education. These clues are commonly gathered from physical facilities (Berry et al., 2006), the perceptions of other university service consumers (Eldegwy et al., 2022a), social life on campus, cost/return on tuition fees (Nanath et al., 2021), and delivery provider attributes such as empathy, responsiveness, and assurance-credibility (Zeithaml et al., 1996). Students who have a positive ‘student–institution right fit’ (Banning & Banning, 1986, p. 1) are expected to make the university choice decision.

A standard industry practice adopted by many universities is to organize onboarding events to allow students the opportunity to gather clues about quality, such as campus visits (Eldegwy et al., 2022b), celebration events (Mulcahy & Baars, 2018), program tasters (Howarth et al., 2016), and deans’ receptions (Secore, 2018). Universities also conduct online (e.g. social media, websites) marketing-related activities, such as influencer marketing, virtual tours, automation, live streaming/webinars, live orientations, and digital newspapers (Elhajjar & Yacoub, 2022). The most prominent online marketing tool is interactive media (Aljumah et al., 2021), such as online taster programs.

Universities offer the opportunity for new students to experience face-to-face interactions with quality staff members in different contexts (e.g. tasters, subject-specific webinars, showcase programs, major exploration events). Online tasters are any online subject-specific program offered by a university that allows students to get a snapshot of the program while having direct experiences with staff members. These short programs, hosted in virtual classrooms, are designed to increase student engagement; they are delivered in seminar-style sessions. Student–staff interaction is enhanced by the electronic platform’s capabilities, such as hand-raising, cameras, audio devices, and webcam filters. Offline forms of taster programs ‘assess the character of the university as [students] interact with staff’ (Brown et al., 2009, p. 13). Tasters often require registration, are offered by invitation, have a maximum capacity to ensure personal interactions, and last for varying durations (throughout a weekend or even a week). Online tasters are the British version of North American showcase programs, and many universities on both continents have adopted this practice.

Conceptual model

The university choice decision can be conceptualized as a consumer buying behaviour in the similar framework of the expectancy-value theory (Eccles et al., 1983) due to the transactional-exchange characteristic that the decision entails. Students incur evident costs to attain university education, which are sacrificed in exchange for a likelihood of success and subject task value to attain the expected benefits of education. Previous works explained students’ university choice decision based on the value-expectancy theory (Gaspard et al., 2019; Guo et al., 2015; Matusovich et al., 2010), which suggests that students expect that sacrificing costs incurred (i.e. time, money, and effort) during their university education will provide value (intrinsic and utilitarian) equal to or greater than the costs incurred. Compared to non-followers, students who find alignment with the subject program (e.g. occupational followers) expect to gain more benefits and incur fewer costs, as suggested by the occupational following theory. Follower students are more likely to augment the value side of the equation and make the university decision. We press the logic forward by drawing on the dominant stream from consumer buying behaviour literature which theorizes the importance of clues for new service consumers to develop expectations of quality (Shostack, 1977) due to the intangible nature of services such as university education.

The described student buying behaviour was constricted by the universal pandemic-related restrictions. The 2020–2021 freshman class was forced to evaluate a service on one of the most extreme points on the intangibility continuum due to the dissolution of the physical campus, leaving them little to rely on as tangible clues of quality. Mainstream consumer behaviour literature advocates the centrality of developing consumer relationships to counter the challenges related to services, most notable of which is intangibility (Elsharnouby & Parsons, 2010; Zeithaml et al., 1996). Building human relationships is suitable for credence-dominated offerings (Zeithaml et al., 1996) such as university education. Among the few clues still available to students in the online modality were interactions with university staff. Students still needed direct experiences to help them form an expectation of the value and the likelihood of success in attaining the values promised/expected from education in both the short and long term (Eccles et al., 1983). Universities’ expenditures to upgrade the necessary digital technology to deliver the marketing (e.g. interactive virtual tours) were high (O’Connor, 2021). Thus, we assume that this clue of quality is not a foreign concept to university administrators.

We selected the context of online taster programs for new students as the clue of staff–student interaction was still present. We adopted the study model (Figure 1) from Navarro et al. (2005) for four reasons. First, the original model investigates antecedents of student satisfaction with a single subject university program which is similar to the context of our study (i.e. tasters). Second, students in both contexts evaluate clues of quality (e.g. staff–student interaction) as a driver of student satisfaction with the program. Third, the unit of analysis in both cases is university students. Finally, the original model captures students’ evaluative response state (i.e. satisfaction with the program) and its cognitive, emotional, and behavioural consequences on the specific program and the university’s overall brand. Our study follows the same theoretical path. The operationalization of the consequences of satisfaction with taster program in the pre-enrolment stage is extra-role behaviour towards the taster program and university brand preference. The rationale for including these consequences of satisfaction is presented in the following sections.

Figure 1. Conceptual model.

We extended the model in two ways. First, we introduced the moderating role of occupational following as we expect the constructs to be stronger for follower than non-follower students when making university choice decisions. Second, we captured actual tuition fee payment as an objective measure of students’ university choice decision.

Hypothesis development

Staff–new student interactions and student satisfaction

Many studies have attested to the role of e-learning platforms in achieving different goals in higher education (Adel & Mahrous, 2018; Adel et al., 2018, 2021). Interactions on these platforms include new students’ interactions with staff during the university onboarding stage. Students perceive direct experiences to be credible due to collecting and processing the information directly through their own devices (seeing, hearing, etc.). The credible nature of direct experiences allows for the formation of higher-order beliefs (Smith & Swinyard, 1982), such as satisfaction. Satisfaction is defined as a cognitive and emotional response to an evaluation process (Giese & Cote, 2000). Students perceive staff to be credible sources of information as they are experienced academics who work within the university and therefore are able to satisfy students’ informational-cognitive needs. Similarly highly cited behaviour attests to the quality of information considered to be credible by consumers when the source is an individual employed within the service provider or with a high level of subject area knowledge (Park & Lessig, 1977); university staff fit both categories. The interaction may also have an affective-emotional dimension. More recently, students were found to be affected by favourable personal trials that evoked positive evaluative responses, and staff exhibiting empathy towards students in online classes increased student satisfaction (Munoz et al., 2022). Accordingly, positive staff–new student interaction, considered a clue of quality, can produce the favourable outcome of satisfaction as students appreciate the informational-cognitive and affective-emotional value of these interactions. Thus, we hypothesize that:

H1: Staff–new student interaction during a taster positively influences student satisfaction with the taster.

Consequences of student satisfaction

University brand preference

Brand preference is ‘a consumer’s predisposition toward a brand that varies depending on the salient beliefs that are activated at a given point in time’ (D’Souza & Rao, 1995, p. 33). This construct is of special relevance to the university decision-making process for two reasons. First, students normally shop around for universities (Moogan et al., 1999), compiling their preferred brands into their choice set, which is ranked in order of brand preference. Eliminating proximity limitations by universities’ digitization of direct experience events may increase the number of universities in the choice set. University brand preference is established from a biased position towards a certain brand demonstrated by certain responses, including affective response (e.g. likeness) and cognitive response (e.g. image). Students’ self-concept motivates them to maintain a relationship with the university program in cases where students’ self-concept and university program attributes share similarities (Desforges & Abouchaar, 2003). Students whose self-concept aligns with the program’s image (i.e. followers) as suggested by the expectancy-value theory will have intrinsic value (Eccles et al., 1983), manifesting in the form of high affective response towards the university brand (e.g. enjoyment). Recent studies have affirmed that students’ satisfaction produces favourable emotional responses (Sun et al., 2021). Accordingly, we hypothesize that:

H2: Satisfaction with a taster positively influences university brand preference.

Students’ extra-role behaviour

Extra-role behaviour is a multidimensional construct constituting recommendation (Arıca & Çorbacı, 2020), positive word of mouth (Bove et al., 2009), advocacy (Yi & Gong, 2013), the provision of feedback to the organization (Gong & Yi, 2019), and a willingness to participate in the firm’s activities (Bove et al., 2009), among other dimensions. The literature has suggested a relationship between satisfaction and extra-role behaviour (Arıca & Çorbacı, 2020; Sharif Nia et al., 2021; Yi et al., 2011). Extra-role behaviour is discretionary consumer behaviour that benefits the organization (Johnson & Rapp, 2010). Expectancy-value theory (Eccles et al., 1983) explains this behaviour. The intrinsic value the student experiences during the online program (e.g. enjoyment) represents a positive memory, which is similar to perceptual re-enactment (Petit et al., 2019), in that, by telling others, the student is stimulating his or her brain to re-enact the representation of that storied memory, which includes re-experiencing the positive emotions. In other words, the student is remembering happy moments which makes the extra-role behaviour a self-serving activity. The motivation to reconstruct the experience through perceptual re-enactment, triggered by storytelling to others, is based on the intensity of the affective dimension of satisfaction. Students who have pleasant experiences during a taster are more likely to want to recall those good emotions than those who have average experiences. Therefore, we presume that satisfaction with a taster has a positive influence on extra-role behaviour. Furthermore, expecting this relationship to be established in both the consumer domain and the employee domain can be extended to students in higher education as students’ membership represents a hybrid of service consumers and organizational members (McNally & Irving, 2010). Students satisfied with the taster will likely display behaviours similar to the extra-role behaviours displayed by students satisfied with the university course program, such as recommending the course to other students (Naheen & Elsharnouby, 2021). We thus hypothesize that:

H3: Satisfaction with a taster positively influences students’ extra-role behaviour.

University brand preference and university decision

Brand preference has a behavioural response outcome (e.g. buying behaviour; Ebrahim, 2013). Expectancy-value theory (Eccles et al., 1983) supports the relationship between brand preference and university decision; in economic terms, the expected return from university education (e.g. graduation) occurs in the distant future. Any future projections are always subject to risk; the longer the projected time interval, the greater the risk. Students will discount the utilitarian value according to their perceived risk associated with the extended interval. The taster allows students to perceive their prospects as more real and relevant. The pre-purchase trial of the university service constructs a perceptual picture of the future, meaning students are not peering into the dark. This mental concept may decrease the perceived risk and its associated discounts on future returns. The affective dimension of brand preference further drives the purchase intention. As previously discussed, some students make their decision based on the institution–student fit. The feeling of fitting in or not cannot be measured objectively. Some students may make the university decision driven by an abstract feeling that they cannot accurately describe. Therefore, students may be highly sensitive to the emotional dimension of brand preference. The taster allows students to develop affective responses, and the brand dimensions conspire to drive the students’ intention to make the university decision. According to Ajzen and Fishbein (2000), attitude predicts intentions, which then predict actual behaviour. Thus, we hypothesize that:

H4: University brand preference positively influences university decision.

Extra-role behaviour and university choice

The manifestations of extra-role behaviour have highly valuable behavioural outcomes, such as repurchase (Mandl & Hogreve, 2019), decreased consumer turnover (Revilla-Camacho et al., 2015), and consumer commitment (Gong & Yi, 2019). Students who have experienced a positive experience are expected to wish to relive those positive experiences and progress to the next level of the program by enrolling in the university (i.e. making the university decision). High school students find the transition from high school to university ‘tremendously exciting’ (Pritchard et al., 2007, p. 323) and are very enthusiastic (Holdaway & Kelloway, 1987). This high level of emotional intensity enhances memory (Dvali, 2017). Therefore, the heightened excitement levels may be the reason for high school students to repetitively wish to relive self-pleasing stories. To continue experiencing positive emotions, students decide to augment the number of positive memories by progressing to the next level of the relationship with the program. These behaviours are operationalized in the university decision context by enrolling in the subject area program. Therefore, we hypothesize that:

H5a: Extra-role behaviour towards a taster positively influences university decision.

Students’ extra-role behaviour and university brand preference

Students are expected to want to continue the relationship with a brand which makes them happy. However, this association between the affective response and the university brand may occur through repetitive perceptual re-enactment (Petit et al., 2019) through the multiple retelling of a student’s positive experience with the taster. The student reaffirms the association between the program and the affective response. Each time the student shares the experience, it stimulates the student’s brain to conceptually re-enact the memory while evoking the associated positive feeling. The student’s relationship strength may increase as an effect of the number of repetitive retellings rather than by fading over time, the way most memories do. A parallel may be drawn to a similar parental behaviour. Parents often have favourite stories about their children’s childhood; they repeatedly share these same stories at almost every social occasion, yet they genuinely laugh as if they have never told them a hundred times before. This story is the stimulus to their brain to perceptually re-enact the memory, which evokes highly affective responses, as if reliving the experience and repeatedly enjoying it. Such experiences are powerful as they stem from the unique parent–child bond at a time when their children were still young and needed them. Similarly, these repetitive emotional associations, evoked by repetitive storytelling, are expected to positively influence the affective dimension of university brand preference. Therefore, we hypothesize that:

H5b: Extra-role behaviour towards a taster positively influences university brand preference.

Moderating role of occupational following

We propose that occupational following moderates the relationship between brand preference and university decision such that the relationship is stronger for followers than non-followers for two reasons. First, social psychology literature reports that individuals tend to manifest more positive responses towards those perceived as being more, rather than less, similar to themselves (Ross, 1971). Similarly, consumers prefer brands they perceive as the kind that would be bought by consumers similar to themselves (Sirgy, 2015). Naturally, occupational followers are second-generation university decision-makers. As university education is a vehicle for intergenerational occupational following, these students are also followers of their parents’ similar educational programs. Followers are expected to prefer the same university programs/brands favoured by other highly similar consumers (e.g. their parents). Support for this similarity argument is found in many cases where children see themselves as not only similar to, but also extensions of their parents (Holmstrom et al., 2011).

Second, people prefer brands that are consistent and/or fit their self-concept (Ross, 1971). Thus, in our context, university brand preference is expected to be stronger for followers than non-followers, as followers’ self-concept will have more in common with the brand than non-followers’ self-concept. Parents play an active role in shaping their children’s self-concept, which may take the form of purposefully offering their children job-specific knowledge and transferring other job-specific skills through daily life interactions (Dunn & Holtz-Eakin, 2000). These trainings and daily exposures are direct experiences which, as suggested by the 10,000-hour rule (Gladwell, 2008), may enhance follower students’ abilities (Crites et al., 1994) and influence their perception of self-concept. For example, follower students reported that studying programs similar to their parents’ occupations augmented self-satisfaction (Martin et al., 2014). Followers’ direct experience with program-specific activities conducted during the taster can help them identify the self-concept that best fits their preferred university brand. The commonality between self-concept and preferred brand is a driver for the behavioural component of brand preference (Grohmann, 2009) such as intention to purchase. Accordingly, we hypothesize that:

H6: Occupational following moderates the relationship between brand preference and university decision such that the relationship is stronger for followers than non-followers.

We further propose that occupational following moderates the relationship between extra-role behaviour and university decision such that the relationship is stronger for followers than non-followers. As previously discussed, followers have a more enjoyable experience during tasters than non-followers due to their innate and/or nurtured personal capabilities. These personal capabilities may be relevant within the taster’s subject-specific context. For example, a follower student attending a fine arts taster program may have inherited some of the painter talents of his or her parents and/or have received painting lessons at home from his or her parents. This follower will be able to shine during the taster activities, experiencing the accompanying self-satisfaction and, therefore, more comfortably engaging with the taster’s teaching staff. These positive emotions may be one reason to expect followers to be more motivated to continue with the university that made them happy during the taster. This process is triggered by the act of storytelling (e.g. saying positive things about the taster program to others), an extra-role behaviour. Repetitive storytelling leads to emphasizing the relationship between the brand and the affective emotion, to the point that the student decides on enrolment to continue this relationship offering emotional value. Accordingly, followers are expected to have higher quality experiences and emotions during the taster than non-followers, enhancing their willingness to select the university. Therefore, we hypothesize that:

H7: Occupational following moderates the relationship between extra-role behaviour and university decision such that the relationship is stronger for follower than non-follower students.

Material and methods

Data collection instrument

We employed previously validated scales from the literature. Some adaptations were needed to ensure the measures were suitable to the higher education context. All constructs were measured using a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree). Mai’s (2005) scale was used to measure staff–new student interaction; students rated the extent to which they believed that staff members provided reliable information, were friendly, and responded in a timely manner. Sivadas and Baker-Prewitt’s (2000) scale was adopted to measure students’ satisfaction with the tasters. We assessed students’ university preference using scales developed by Ebrahim (2013), which rated the extent to which they preferred one university over another, considered it their first choice, and intended to pay tuition. We captured extra-role behaviour using a scale adopted from Eldegwy et al. (2018), which was originally developed by Paswan and Ganesh (2009) and Netemeyer et al. (2004). Finally, university decision was determined using a 2-point nominal scale obtained from the university’s system based on the student’s fee payment. Items for all scales are included in Table 2.

Table 2. Constructs and findings of confirmatory factor analyses.

Constructs	Operationalization of the constructs	λ	T	CR
Staff–new student interaction	The staff is friendly.	.89	–	0.75
	The staff is supportive.	.67	10.41
	The staff provided me with reliable information.	.64	10.13
Student satisfaction with taster	Overall, I am very satisfied with this program.	83	–	0.79
	This program has met my expectations.	.68	12.02
	This program has met my needs.	.69	12.08
University brand preference	I have decided this is my preferred university regardless of any other university's decision.	.81	–	0.76
	This university is my preferred choice over other universities.	.77	11.31
	I will pay my university fees after this program.	.57	9.59
Student Extra role behavior	I will say positive things about this program to others.	.71	–	0.80
	I will encourage other students to participate in this program.	.91	11.76
	I am willing to pay a higher price (time and effort included) for this university's program than any other programs at another university I know about.	.62	10.90
	I am willing to pay more (time and effort included) in order to get into the subject area of the same program.	.58	10.21

The questionnaire used to collect data was developed using a stepped approach. The initial step was a literature review, and an exploratory study was conducted to develop the questionnaire’s first version. This questionnaire was then submitted to a panel of marketing professors and 16 students to ensure relevance, clarity, and validity. The final questionnaire contained 13 questions. In addition, a 14th question concerning university decision was answered using data from the university’s Oracle system. Students were identified as followers/non-followers through their application data after obtaining their consent (parents’ higher degree qualifications data was compared to the undergraduate program; i.e. applying to similar or different program of study as their parents). In addition, demographic data was collected, including gender, age, and program of study (Table 1). As per the demographic data presented in Table 1, the majority of the sample were 18 or 19 years old, accounting for 91% of the sample, while 20- and 21-year-old students accounted for 9%. Females represented 52% of the sample compared to 48% for males. Finally, 40% of the students represented in the sample were applying to health and life programs, 31% were applying to engineering and technology programs, and the remaining 29% were applying to social sciences programs.

Table 1. Sample description.

	Followers		Non-followers
	Frequency	Percentage	Frequency	Percentage
Gender
Female	52	51.3	139	52.4
Male	49	48.7	127	47.6
Age
18	28	29.4	69	26.6
19	67	64.7	172	65.7
20	5	4.9	19	6.5
21	1	0.9	6	1.2
Program
Social Sciences	45	44.5	61	22.9
Engineering & Technology	33	32.6	80	31
Health & Life	23	22.7	125	46.9

Research design, data collection, and sample

Context of study

The current study was conducted in the context of higher education in Egypt. The importance of the higher education industry as a driver of both economic growth and scientific knowledge cannot be understated. The appreciation of the vitality of this industry was reflected in the actions of Egyptian policymakers who passed legislation allowing for the introduction of private universities in the higher education landscape (Eldegwy et al., 2018) in order to create admission slots to the growing young population characteristic of Egyptian demographics (El Khouli, 2015). Some public universities have been described as quality compromisers who aim to serve a large number of students while suffering from a chronically budget deficiency (Mahrous & Kortam, 2012). On the other hand, private universities are mostly for profit in nature and have been described as market-oriented universities which customize their offerings by listening to their stakeholders; hence, they are categorized as customizers (Mahrous & Kortam, 2012). These private universities are in fierce market competition for undergraduate tuition fees which constitute their exclusive source of revenue (El Sheikh, 2019). This competition has been exacerbated due to the increasing number of new university entries each year. The first four private universities opened their doors in 1996, with the current number of competing private universities standing at 32 in 2022 (Ministry of Higher Education, 2022). A closer examination of private universities reveals the high degree of variance in tuition fees. For the purposes of this study, we ranked the five most expensive universities registered under the Ministry of Education’s umbrella of direct accreditation. These universities charged annual tuition and fees exceeding 75,000 LE ($5000). The remaining 27 universities fall in the low-fee category. We expected higher-fee universities to employ the highly responsive marketing practices necessary in the face of the abrupt nature of the pandemic. Our logic was that higher-fee universities still needed to showcase their clues of quality in the online domain to justify the price differential compared to low-fee universities. Therefore, these universities were expected to have an urgent need to offer online onboarding programs (i.e. tasters). Our investigation revealed that only one university in both the high- and low-fee categories employed online taster programs. Many other universities in both categories employed direct experience events, such as live streams, webinars, and animated campus tours, to attract future students, whereas public universities did not undertake formal marketing recruitment activities.

We decided to select the taster program due to its maximum capacity criteria, structured format, and pre-registration which ensures that the participants were students rather than family and friends. The taster-offering university agreed to participate in this study and granted the first author access to its database to get actual registration data. Therefore, data was collected from various sources, including secondary sources (e.g. university records) and surveys collected through the participating university’s electronic platform.

The participating university invited students to attend tasters with selected staff. Students could choose to attend tasters in different subject areas. We believed that the non-obligatory, optional attendance nature of tasters offered exclusively to prospective students in the post-application period at nominal fees meant they would be attended by those who wanted to gather clues about quality to calibrate their cost–benefit equation. This framework of the expectancy-value theory allowed us to assume that students who believe an institution–self fit exists and who have positive value would enjoy the taster and see it as a trial run of how well they would adapt and perform within their new roles as university students. The tasters’ topics are selected to allow for high student engagement and do not require special tools or equipment (e.g. engineering students only need access to a computer to actively participate in online shop creation of their tasters). The programs lasted from one full day (8:30–3:30) to 3 days. At the end of the program, students were asked to complete the questionnaire by clicking on a link. Only students who attended tasters were included in the study. Students’ consent was obtained, and the nature of the study was explained to them prior to their participation. Students were asked to evaluate their experiences after the tasters, thereby ensuring that their experiences were recent. Data was accumulated and analysed against the backdrop of actual student behaviours (i.e. joined the university or not). At the end of the admission period, 1150 questionnaires were saved on the university system. Only questionnaires linked to new students who received an offer of acceptance to the university were included in the study to ensure that the university decision was the student’s own. In addition, a qualifying question about the number of universities to which students applied was included; those who applied only to the participating university were excluded to ensure that the sample was drawn from new students interested in several universities. Academic eligibility and the number of universities considered reduced the number of qualified questionnaires to 377. Ten additional responses were excluded due to missing data, resulting in a final total of 367 usable surveys. The follower group accounted for 101 students while non-followers accounted for 266 (Table 3).

Table 3. Correlation matrix and average variance extracted scores for all constructs.

Constructs	AVE	Staff–new student interaction	Student satisfaction with taster	University brand preference	Student Extra role behavior	University choice decision
Staff–new student interaction	0.50	1
Student satisfaction with taster	0.54	0.792	1
University brand preference	0.53	0.395	0.428	1
Student extra role behavior	0.52	0.584	0.645	0.583	1
University choice decision	na	0.288	0.306	0.450	0.422	1

Note: AVE, average variance extracted.

Results

Structural equation modelling (SEM) was adopted with the aid of AMOS 20 to statistically analyse the data. We assessed the psychometric properties of the measuring scales using the reliability and validity of the model constructs. Composite reliability indices ranged between 0.75 and 0.80 for all scales, indicating satisfactory internal consistency. The average variance extracted (AVE) for all model constructs surpassed the suggested threshold of 0.50 (Nunnally & Bernstein, 1994).

We acknowledge that the validity of the results was threatened by common method bias as the data was collected using only one questionnaire; therefore, we undertook the following procedures to mitigate this threat. First, the data was collected from two different data sources: the survey and the university Oracle system. Second, all respondents were informed that their responses were for research purposes, that there were no right or wrong answers, and that their responses had no effect on their application status. Third, respondents who applied to only the participating university were excluded, as we intended for the sample group to include students who had experience with the admission process of at least one other university (i.e. were familiar with other university brands and brand offerings). Fourth, we informed respondents that their responses would be used to further improve the experience of future applicants by improving taster experiences; such improvement is impossible in the absence of valid data, so applicants may appreciate their role in improving the experience of their friends and relatives who may wish to enrol in the taster in the future. Finally, we explained the actual university decision supported by actual fee payment through follower and non-follower students’ self-reported responses. This approach is noteworthy, as the marketing literature has commonly reported discrepancies between intentions and actual behaviour, as described in terms of the attitude–behaviour gap (Claudy et al., 2013). This gap may be caused by the overlapping life engagements following the onboarding programs, which prevent some students from materializing their university choice intentions and thus influence the actual university decision.

According to the confirmatory factor analysis (CFA) results, all variables had standardized factor loadings greater than 0.50, demonstrating sufficient convergent validity. Finally, all model fit indices were satisfactory. The measurement model’s goodness-of-fit measures were as follows: χ2 = 144, df = 45, p < 0.001, χ2/df = 1.51, IFI = 0.98, TLI = 0.975, CFI = 0.981, and RMSEA = 0.037. The path model also exhibited an adequate fit (χ2 = 7.41, df = 3, p < 060, χ2/df = 2.475, GFI = 0.992, IFI = 0.99, TLI = 0.98, CFI = 0.99, RMSEA = .063). Therefore, we proceeded to examine the hypotheses.

As indicated in Table 4, staff–new student interaction had a significant effect on student satisfaction with the tasters (β = 0.49, p < 0.001), thereby supporting H1. Staff–new student interactions explained 24% of the variation in student satisfaction with online subject programs. The results also supported H2. Satisfaction with online subject programs had a significant effect on university brand preference (β = 0.18, p < 0.05), with 35% of the variation in university brand preference explained by students’ satisfaction with the taster. Satisfaction with the taster had a significant effect on students’ extra-role behaviour (β = 0.65, p < 0.001), supporting H3, as 62% of the variation in students’ extra-role behaviour was explained by students’ satisfaction with the taster. H4 was also supported as university brand preference had a significant effect on university decision (β = 0.31, p < 0.001). The results also supported H5a; extra-role behaviour had a significant effect on university decision (β = 0.24, p < 0.001). H5b was supported as extra-role behaviour had a significant effect on university brand preference (β = 0.43, p < 0.001). The two predictors of extra-role behaviour and university brand preference explained 24% of the variation in university decision.

Table 4. Structural model results. Followers, 101, Non-Followers, 266.

Hypothesized paths	Group name	Beta	t-value	Hypothesis result
H1: Staff–new student interaction during tasters – student satisfaction with the taster.	All	0.49***	10.7	Supported
	Followers	0.75***	12.6
	Non Followers	0.65***	13.2
H2: Satisfaction with the taster – university brand preference.	All	0.18*	2.4	Supported
	Followers	0.55***	6.9
	Non Followers	0.25***	4.4
H3: Satisfaction with the taster – student extra-role behavior toward taster.	All	0.73***	24	Supported
	Followers	0.98***	10
	Non Followers	0.82***	7.8
H4: University brand preference – university choice decision.	All	0.31***	5.5	Supported
	Followers	0.57***	8.5
	Non Followers	0.41***	7
H5a: Extra-role behaviour towards taster positively influences university brand preference.	All	0.43***	5.5	Supported
	Followers	0.27***	3.9
	Non Followers	0.40***	7.0
H5b: Extra-role behavior toward taster – university choice decision.	All	0.24***	4.32	Supported
	Followers	0.48***	5.2
	Non Followers	0.52***	4.7
R^2
Satisfaction with taster	All	0.24
	Followers	0.56
	Non Followers	0.42
University brand preference	All	0.35
	Followers	0.57
	Non Followers	0.29
Extra-role behavior toward taster	All	0.69
	Followers	0.33
	Non Followers	0.13
University choice	All	0.24
	Followers	0.65
	Non Followers	0.29

Note: Sig. at *** P < .001, *P < .05.

Moderating effect of occupational following

We conducted a moderation analysis utilizing Hayes’s (2018) PROCESS module to examine whether occupational following moderates the relationship between both extra-role behaviour and university brand preference and university decision. Our results indicated that occupational following moderates the relationship between brand preference and university decision (β = 0.25, t = 3.46, LL = 0.11, UL = 0.39), offering support for H6 (see Table 5). We examined the moderating effect strength. Our figures indicated that the interaction effect of occupational following on the relationship between university brand preference and university decision significantly increased the effect by 0.25, resulting in a sufficient effect size.

Table 5. Results of moderator test.

A. Moderator test for occupational following between university brand preference and university choice
	Moderator effect	SE	Lower limit	Upper limit
University Brand preference	0.99***	0.12	0.75	1.24
Occupational following	1.46***	0.25	0.96	1.95
Occupational following x Brand preference	0.25***	0.07	0.11	0.39
B. Moderator test for following between extra role towards taster and university choice
	Moderator effect	SE	Lower limit	Upper limit
Extra role behavior towards taster{	0.48***	0.14	0.20	0.76
Occupational following	1.00***	0.29	0.43	1.58
Occupational following x Extra role behavior towards taster	0.16**	0.07	0.07	0.31

Note: Sig. at *** P < .001, **P < .01.

Furthermore, our results indicated that occupational following moderates the relationship between extra-role behaviour and university decision (β = 0.16, t = 2.15, LL = 0.07, UL = 0.31), offering support for H7 (see Table 5). In addition, we investigated the moderating effect’s strength. Our figures reveal that the interaction effect of occupational following with extra-role behaviour caused an increase in the effect by 0.16, resulting in a sufficient effect size.

Conclusions and implications

Conclusions

Most of the hypothesized direct relationships were supported, as expected. Specifically, good quality interaction with service providers–academic staff leads to student satisfaction with tasters (H1), university brand preference is affected by taster satisfaction (H2), and taster satisfaction leads extra-role behaviour (H3). However, this study’s investigation of occupational following within the university choice context offers several new contributions to the literature. The results demonstrate that followers assign more significance to online interactions with staff during tasters than non-followers (see Table 4), thereby impacting satisfaction with the taster. In other words, quality online interactions with staff are likely to satisfy followers more than non-followers within the taster. Some follower students have human capital (e.g. knowledge and skills) transferred through exposures to their parents’ professional occupations. Followers are, therefore, at an advantage to have higher participation and involvement levels during tasters. Participation and involvement are crucial for the attainment of desired outcomes from any interactive learning program (Zhao & Kuh, 2004). Thus, followers are expected to have more satisfying interactions within tasters.

Our results also provide evidence of the moderating effect of occupational following on the relationship between extra-role behaviour and university decision (H7). It is plausible to attribute the increase in effect to the higher level of subject area knowledge, transmitted through exposures to parents’ professional occupations. Accordingly, compared to non-followers, followers may be expected to experience several advantages during their taster experience, such as ease of comprehending new concepts introduced during tasters, self-satisfaction with personal abilities, and overall positive emotional experience. Consequently, followers are expected to enjoy telling others about their positive experiences to recall their positive emotions, which is identical to the advocacy behaviour characteristic of students’ extra-role behaviour (Yi & Gong, 2013). In other words, satisfied followers are more likely than non-followers to recommend the taster to others, as shown by the difference in effect size between the two cells in Table 4. Having had a comparatively more memorable and enjoyable experience than non-followers, followers are more likely to display highly valuable behavioural outputs which would allow them to continue the relationships that they enjoy – that is, make the university decision. This is supported by the interaction effect of occupational following on the relationship between extra role and university brand preference presented in H7.

The results indicate that the model has significantly different explanatory powers of university decision in the followers group (65%) compared to the non-followers group (29%). This difference is an indication of missing antecedents in the model that are more important to non-followers than followers to explain the variance in university decision. These results may be counterintuitive as followers have a higher level of knowledge of the university program than non-followers and are expected to be critical in their evaluations. In other words, followers have program-specific knowledge gained through exposure to their parents’ jobs and, accordingly, should undergo a more thorough evaluation of program benefits/attributes than non-followers prior to making the university decision. However, novice consumers (i.e. non-followers) may not have developed their service expectations (East, 1992). We find a possible explanation for this finding in the abstract notion of student–institution right fit (Banning & Banning, 1986) where some students describe the decision as where ‘a person like me will feel comfortable’ (Canterbury, 2000, p. 19) rather than an objective evaluation and selection of the best university. The final university choice is ‘based as much on a feeling of wellbeing, or at least a lack of discomfort as any rational calculation’ (Canterbury, 2000, p. 17). Feelings as emotional drivers of followers may be stronger than previously discussed in the alignment of self-concept (Eccles et al., 1983), with the university programs leading to occupational following. These follower students may possess a high level of appreciation for the intrinsic and emotional values, which they perceive in the university programs. Followers may be more receptive to the emotional dimension of brand preference than non-followers (H7). Emotions produce irrational tendencies, as consumers in some cases ignore their rational thinking and select their favourite objects (Tsai et al., 2015) based on irrational tendencies, such as the exclusion of important university decision antecedents. As a result, we find in the emotional brand preference dimension a possible justification for the different explanatory power of the model between the two cells.

Managerial implications

This study offers several managerial implications to universities in their quest to enhance student recruitment practices. First, parents’ occupational following is premised on the child aspiring to voluntarily choose to follow in the parents’ footsteps (Laband & Lentz, 1983), which is rational in the case of high status parents (Sputa & Paulson, 1995), thereby allowing for the inter-generational transfer of social status. Therefore, it is expected that some parents of followers are highly valuable potential partners who can strengthen the link between academia and industry. Universities can seek help from follower parents by offering non-followers internships and other experience-based training. It is also a normative practice to offer high-performing interns a full-time job. These efforts are premised on the support of followers and their parents for non-followers. Follower parents will likely be interested in assuming these responsibilities. Follower parents understand that their children who graduate are university products and represent the university’s brand (Canterbury, 2000). Naturally, parents are highly invested in their children’s success and, by inference, the university brand success. By the same logic, some follower parents may be inclined to support other graduates who represent the same university brand, as these non-follower graduates also represent the same brand as their children. Our recommendation is for universities to appreciate the value of follower parents and design programs which can engage them.

Second, staff may find it useful to design coursework and assignments suitable for group work and assign follower students to different groups so they can help support and motivate their colleagues by sharing their experiences with their classmates. The brand preference, with its lower-level component of affective brand component, is stronger with these follower students (H6), who are more likely to continue to exhibit a high level of motivation and engagement, thereby decreasing the likelihood of failure and dropout. Furthermore, some parents of followers home tutor their children (Desforges & Abouchaar, 2003), implying that some follower students are at an academic advantage which allows them to support their non-follower colleagues. In fact, at some universities, parents have been recruited as co-tutors (Mulcahy & Baars, 2018).

Finally, this study offers insights to a specific category of universities – namely, universities which are on students’ shopping list, but are not their preferred choice. These universities typically experience high numbers of applications but suffer from low application conversion rates to university choice decisions. The inclusion of these types of universities in the new student’s consideration set does not necessitate a top-brand preference rank. Marketers’ decision to offer tasters in an effort to affect the university brand preference dimension in the minds of their prospective students seems to have a strong impact on university choice decision (H2). Furthermore, extra-role behaviour indicates a willingness to recommend components (H3), allowing universities to spread their brand message through new students satisfied by their experiences during the taster at minimal cost.

Limitations

We present four main limitations of this study which may highlight areas of future research. First, this study draws on Western-dominated literatures which cast the students in the role of decision-makers in regard to university decision and parents in the role of influencers (Whitehead et al., 2006). However, the collective nature and different power structure of families (Burgess & Steenkamp, 2006) and recent works suggest that parents are co-decision-makers in Eastern cultures (Eldegwy et al., 2022b). Therefore, future studies that account for the more rational group decision-making behaviour criteria within the Eastern context are advised. Second, the literature attests to the importance of other quality clues which students evaluate before making the university decision. However, due to the forced migration to online domains for most human interactions in education due to the pandemic, this study only included staff–student interaction. Future studies may find it prudent to include additional clues of quality offered through the brick-and-mortar contextual environment of universities. Third, the sample was collected from the new student pool of a single university as this was the only university that had a formal onboarding program in Egypt (i.e. offered online tasters). We acknowledge that online tasters are common in many universities in the West; however, due to resource limitations, we were unable to include students from other universities in different countries. Therefore, caution is advised when generalizing the findings beyond the boundaries of this study’s sample. Finally, even mainstream buying behaviour models suffer from low explanatory power in certain contexts, such as the military (Whetten, 1989). Conversely, a model’s explanatory power may be augmented due to the context of study. We presume that the context of the unique nature of the parent–child relationship prevalent in the case of parent–child occupational following is such a context. We attributed the difference in the explanatory power of the model between both cells (i.e. followers 65%, non-followers 29%) to followers’ higher intrinsic emotional value; however, this emotional dimension may also be dependent on factors beyond the justifications offered by the value-expectancy theory, including the quality and nature of the parent–child relationship. This argument may find some support in previous works reporting on students refusing to become occupational followers on the grounds of wanting to do something different than their parents (Brooks, 2003). However, we cannot substantiate this claim and therefore present this speculation as an area of future research.

Acknowledgements

Open Access made available by October University for Modern Sciences and Arts (MSA University).

Disclosure statement

No potential conflict of interest was reported by the author(s).