The Influence of Consensus Information on Rejected Applicants’ Organizational Attractiveness Perceptions and Recommendation and Purchase Intentions
Name: Chrystel Vijzelman Student number: 10730753
Date of submission: 21-07-2017 final version Study: Executive Programme in Management studies Specialization; Strategic Marketing Management track Institution: Amsterdam Business School
Statement of originality
This document is written by Chrystel Vijzelman who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of contents
Abstract ... 4
1. Introduction ... 5
2. Literature review ... 8
2.1 Employer branding and employer brand ... 8
2.2 Corporate branding and corporate brand ... 10
2.3 Relationship between employer branding and corporate branding ... 11
2.4 Selection feedback ... 12 2.5 Selection fairness ... 13 2.6 Organizational attractiveness ... 15 2.7 Brand equity ... 16 2.8 Recommendation intentions ... 18 2.9 Purchase intentions ... 19 2.10 Research question ... 20 2.11 Hypotheses ... 20
2.12 Conceptual model of relationships... 23
3. Method ... 23
3.1 Pre-test study... 23
3.1.1 Research design and procedure of pre-test study ... 23
3.1.2 Sample of pre-test study ... 24
3.1.3 Measures of pre-test study ... 25
3.2 Main study ... 26
3.2.1 Research design and procedure of the main study ... 26
3.2.2 Sample of main study ... 27
3.2.3 Measures of main study ... 28
4. Results ... 28
4.1 Pre-test study... 28
4.1.1 Correlation analysis pre-test ... 28
4.1.2 Analyses pre-test study ... 29
4.2 Main study ... 31
4.2.1 Correlation analysis main study ... 31
4.2.2 Hypotheses testing ... 32
4.2.3 Additional mediation analyses ... 34
5. Discussion ... 40
5.1 Limitations ... 43
5.2 Implications and further research ... 44
References ... 47
Appendix 1 ... 53
Abstract
This study aimed to examine the impact of consensus information on selection fairness, which in turn was expected to have an impact on an organization’s attractiveness, brand equity, and applicants’ recommendation and purchase intentions. The data for the main study was
collected from 142 rejected applicants at a Dutch insurance company. Prior to the main study a pre-test study was performed. Most studies on the topic of selection fairness and the effects of selection fairness have been done in a laboratory setting, whereas the present study has been done in an actual application environment, with actual applicants. We found that rejection emails containing consensus information improved the perceived organizational attractiveness among rejected applicants. Furthermore, higher organizational attractiveness among rejected applicants in turn improved brand equity. Finally, it was found that this higher brand equity had a positive impact on both recommendation and purchase intentions among rejected applicants.
1. Introduction
Nowadays, companies recruit potential applicants on a much larger scale than before. According to a survey carried out by The Future Workplace, this is partially the result of millennials’ frequent job-hopping. An estimated ninety-one percent of millennials (individuals born between 1977 and 1997) assume to keep a job for less than three years before switching to another job (Meister, 2012). Also, according to the Pew Research Center (2010), millennials seem to be especially prone to switching jobs or careers, and an estimated sixty percent of employed millennials have already switched jobs at least once in their
careers. The same research also discovered that six out of ten employed millennials thought it very unlikely they would keep working at the same employer for their entire career. This “job hopping,” particularly early in a career, confirms this common millennial stereotype among many human resource specialists (Thompson & Gregory, 2012).
As a consequence, employees in general are continuously investing a lot of time and effort in applying for new jobs and thus undergo many steps in selection processes (e.g., sending in resumes and motivation letters, taking personality, IQ-, and other tests, going on interviews). So, because employees change jobs more frequently than before, they constantly adapt their motivation letters to the specific vacancy. Therefore, these job applicants expect a certain basic level of courtesy in return. For example in the form of at least acknowledging receipt of the jobseekers’ applications and informing them about the selection decisions, but also motivation of a rejection decision.
Due to this large scale job-hopping, many individuals have to deal with being rejected by organizations, often in a perceived inadequate manner. However, up to date, organizations do not give sufficient attention to the negative consequences of rejecting large groups of individuals on a continuing basis. Examples of such negative consequences are: lowered organizational attractiveness in the eyes of the applicant, and decreased intentions to
recommend the organization to others (e.g., other potential applicants, potential clients) (Bauer, Maertz, Dolen & Campion, 1998; Hausknecht, Day & Thomas, 2004; Schinkel, van Vianen & Ryan, 2016; Schinkel, van Vianen & van Dierendonck, 2013).
Further, one large practical survey study has also shown decreased intentions to purchase the organization’s products or services (Stevens, 2010). This survey study (N = 1600), conducted by talent assessment specialists SHL, describes the negative consequences of these companies’ inadequate response to job applications. In this practical study conducted in the UK, it was shown that half of the jobseekers that participated reported a negative impression of the organization after an unsuccessful job application. Furthermore, one fifth of all rejected participants stopped purchasing its products or services as a result. The study found that the biggest issue for the job candidates who participated in this study was that they were ignored by organizations where they had applied to work for. Results showed that forty-six percent of respondents not being informed by the employers’ selection decision was worried by this. Furthermore, lack of feedback on the application (thirty-nine percent) and not confirming receipt of the application (thirty-six percent) were other major issues for the job applicants.
One factor that may influence applicant reactions is indeed the feedback that is provided by the organization about a particular selection decision. A few studies have investigated the influence of different aspects of feedback (type, tone, favorability) on applicant reactions (e.g., self-evaluations, organizational attractiveness) (Ployhart, Ryan & Bennett, 1999; Ployhart, Ehrhart & Hayes, 2005; Schinkel et al., 2016). These studies have shown that type of feedback and the tone of the feedback message affect applicants’ reactions to a rejection. For instance, providing information about the amount of other applicants that applied, named consensus feedback, has been found to positively influence rejected
Further, previous scientific research has shown that applicants’ reactions to a rejection are influenced by their perceptions of the fairness of that decision. Selection Fairness Theory, developed by Gilliland (1993), postulates that applicants’ cognitive, affective, and behavioral reactions to selection procedures and decisions are (partly) determined by the extent to which they think these are conducted in a fair manner. Indeed, it has been found that job applicants who experience their selection outcome (hire or reject), or the preceding procedure, as unfair perceive these organizations as less attractive than those who have more positive experiences (Schinkel et al., 2013; Schinkel, et al., 2016). This organizational attractiveness refers to the perceived attractiveness of the organization as an employer.
It has further been found that organizational attractiveness is related to
recommendation intentions (Bauer, et al., 1998; Hausknecht et al., 2004; Schinkel et al., 2013; Schinkel et al., 2016). This means that applicants, who perceive the organization that rejected them as less attractive, will also be less inclined to recommend it to others (potential
applicants, clients). Following from this, it seems likely that these applicants will also be less inclined to purchase its products or services. Indeed, this link has been shown in a practical study (Stevens, 2010). However, no scientific research in the applicant reactions context has been done on the relationship between organizational attractiveness and purchase intentions (Hausknecht et al., 2004). This means a gap still exists in knowledge on the link between the employer branding side and the consumer buying side. That is, it is of managerial relevance to further explore the impact of rejecting large groups of applicants on their reactions towards recruiting organizations (e.g., recommendation intentions and purchase intentions).
Further, more research is needed into which type of rejection feedback causes the least unfavorable applicant reactions. A possible solution to study this issue is to combine applicant reactions research with research from a branding perspective. For instance, brand equity might be important in this process. In general, brand equity refers to the marketing effects that
can be uniquely attributed to the brand (Keller, 1993). Several types of brand equity exist, such as customer based brand equity and employer based brand equity (Keller, 1993). Employer branding in the context of recruitment pertains to the total of psychological, economic, and functional benefits that potential employees associate with being employed at a particular organization (Wilden, Gudergan & Lings, 2010). If organizations are aware of these perceptions, they can utilize this knowledge to create a more attractive and competitive employer brand, which can lead to an increased value of the corporate brand (Wilden et al., 2010). In the next chapter, the main variables of this study and their relationships will be discussed.
2. Literature review
In this chapter we will first discuss two important aspects of branding, within which the present research will be framed, namely 1) employer branding, and 2) corporate branding. Subsequently, the relationship between these two aspects of branding will be discussed. Next, all main variables of the present research will be further defined and discussed. In order to be able to answer the stated research question several variables need to be further explored. The variables that will be further discussed are: feedback, perceived selection fairness,
organizational attractiveness, brand equity, recommendation intentions, and purchase intentions.
2.1 Employer branding and employer brand
According to Davies (2008, p.2) “an employee or employer brand is a set of distinctive associations made by employees (actual or potential) with a particular corporate name”. A strong employer brand results in the attraction of better applicants (Collins & Stevens, 2002; Slaughter, Zickar, Highhouse & Moore, 2004) and also shapes these applicants’ expectations about their employment (Lievens & Highhouse, 2003). Employer branding refers to
their own employees (internal) as well as for potential applicants (external) (Backhaus & Tikoo, 2004). Both types of employees form a certain brand image of an employer, partly based on the brand associations that are the result of the firm’s employer branding.
Importantly, however, they also form employer brand associations from information sources that are not controlled by employers (Backhaus & Tikoo, 2004). The impact of employer branding on both potential and current employees is also described by Dell, Ainspan,
Bodenberg, Troy and Hickey (2001, p. 10), who state that “the employer brand determines the firm’s identity as an employer. It includes the firm’s values, systems, policies, and behaviours toward the objectives of attraction, motivation and retention of a firm’s current and potential employees.” According to Wilden et al. (2010), this means that organizations can reduce uncertainties and risks experienced by potential employees by purposefully designing an employer brand aimed at the employee market. Thus, effective employer branding focuses on identifying desired brand associations and then aiming to create these brand associations (Backhaus & Tikoo, 2004).
According to Wilden et al. (2010), it is necessary for organizations to develop strategies, which give certainty that their human-resource base remains suitable for the challenges that go hand in hand with doing business. In markets where competitive
employment is continuously increasing, focusing on the development of strategies to become an employer of choice and also increasing the total of applicants per advertised vacancy can result in easier recruitment of suitable employees and therefore provides a strategic advantage to the firm. By applying a resource-based view to the organization, the importance of tangible resources such as human capital as a source of competitive advantage will become more obvious (Amit & Schoemaker, 1993; Hanson, Dowling, Hitt, Ireland & Hoskinson, 2001), and increasing the attraction of qualified staff becomes of strategic importance to the firm. In line with this, Branham (2001) proposes employer branding as a method to ensure access to
potential employees. Because employer branding and consumer or corporate branding may be highly related, in the next section corporate branding will be discussed in further detail. 2.2 Corporate branding and corporate brand
According to Abratt and Kleyn (2012), the corporate brand consists of two aspects, which are: 1) corporate expression and 2) stakeholder images of the organization’s identity. Corporate expression includes all mechanisms employed by the organization to express its corporate identity to all stakeholder groups. Corporate expression connects the organization’s corporate identity with its corporate brand and therefore corporate expression is categorized as part of both constructs. In order to determine the corporate expression, organizational leaders must make certain strategic choices such as the aimed impression and communication of an organization’s visual identity, its brand promise, and its brand personality.
The second aspect of corporate branding refers to stakeholders’ perspectives of an organization’s brand. A stakeholder can never connect with an organization’s corporate identity in its whole. A stakeholder solely affiliates with separate aspects of the organization’s identity and through these certain aspects forms a certain perception of the corporate brand. When stakeholders experience a certain brand, they develop certain brand images about the brand. Even though every stakeholder will have an experience with the brand, only a few will develop relationships with the brand or even join brand communities. During these
interactions, stakeholders will form certain judgments about a brand by contemplating to what extent the brand has actually delivered the brand promise. Hereafter, the stakeholders will evaluate the brand’s personality in comparison with their expectations and requirements (Abratt & Kleyn, 2012).
During the development of an organization’s corporate identity and the corporate brand, it is important to recognize the fact that some stakeholders are more important to the organization than others. If the goal of the organization is to change stakeholder’s images and
perceptions regarding the organization’s reputation, it will be necessary for the organization to change some aspect of its strategic choices, or its corporate expression. Either parts of the organization’s corporate identity will have to be adjusted or tailoring the brand expression will be necessary (Abratt & Kleyn, 2012). In the next section, the relationship between employer branding and corporate branding will be discussed.
2.3 Relationship between employer branding and corporate branding
According to Balmer and Gray (2003), a strong, favorable corporate brand is a
powerful “guiding tool” to various stakeholders, such as existing employees, shareholders, but also potential employees. According to Wilden et al. (2010), the most frequent brand
definitions focus mainly on customers and not on other stakeholders, for example potential employees, who are also influenced by brand messages.
In an employer marketing context, the employer brand refers to the set of distinctive images of a potential employer, which are visible in the minds of the target groups, which are potential employees (Meffert, Burmann & Koers, 2002; Petkovic, 2004). That is, due to certain stakeholders having a strong impact on corporate brand management, aligning corporate branding with employer branding has become increasingly important (Foster, Punjaisri & Cheng, 2010). In this case, potential applicants are also the potential customers of an organization, and according to corporate management perspectives both are considered to be the important external stakeholder audiences of an organization (Knox & Freeman, 2006). Thus, without aligning the corporate brand with the employer brand, inconsistencies may develop among the general public about how the brand or organization is perceived, which may result in the corporate brand having a negative impact on the employer brand and vice versa (Moroko & Uncles, 2008).
It can be a difficulty for organizations to simultaneously manage the various brands they present to their various stakeholders (e.g., company brand, employer brand, consumer
brands). In the context of the present study it is important to analyze the link between the Human Resource Management function and the marketing function (Martin, Beaumont, Doig & Pate, 2005). It is crucial for both marketing specialists and Human Resource Management specialists within an organization to realize the negative consequences of their actions on each other’s branding objectives. Therefore, it is essential that the marketing and HR-employees align their efforts as much as possible.
The employer brand may influence all of the other brands of the organization, and for that reason aligning the organization’s internal beliefs and external brand messages is crucial (Wilden et al., 2010). Therefore, organizations should take into account the impact of
rejecting applicants: this may affect rejected applicants’ intentions to recommend the
organization to other applicants and their intentions to buy (keep buying) these organizations’ products or services.
Two factors that have been demonstrated to influence applicants’ reactions are
selection feedback and perceived selection fairness. These factors will be discussed in further detail in the next two paragraphs. After that, the applicant reactions that will be studied in this paper will be discussed: organizational attractiveness, brand equity and recommendation and purchase intentions.
2.4 Selection feedback
Little research has been done regarding the effect of explanations about selection decisions on applicant reactions (Ployhart et al., 1999). One of the few researchers who studied this topic are Ployhart et al. (2005). In their research they used the covariation model, which was developed by Kelly (1987). This covariation model can be used in order to better comprehend the effects of explanations because it clarifies how people use various types of information when they make causal conclusions. The covariation model consists of three dimensions, namely consensus, distinctiveness, and consistency. As Ployhart et al. (2005)
found that consensus information was the most critical factor in influencing applicants’ reactions, we have chosen to focus on this dimension.
Consensus information in the applicant reactions context refers to a certain piece of information indicating whether other applicants received the same treatment (i.e., rejection) during or after the application process as the individual (Ployhart et al., 2005). This entails the following: high consensus takes place when the majority of the applicants receive the same outcome as the individual (e.g., the majority is also rejected), while low consensus takes place when a small number of applicants receive the same outcome (e.g., a minority is also
rejected). Ployhart et al. (2005) conducted two studies in which they measured the influence of different types of consensus information on fairness perceptions and organizational attractiveness among rejected applicants (students applying to universities). They found that high consensus information (i.e., in the rejection message, an applicant receives the
information that many other applicants have also been rejected) resulted in higher selection fairness perceptions and subsequent higher organizational attractiveness among rejected applicants.
2.5 Selection fairness
According to Gilliland (1993), rejected individuals who perceive a selection outcome to be unfair or to be the result of biased procedures, may perceive lower organizational attractiveness than those who experience higher fairness perceptions after their unsuccessful application. The first group may experience feelings of anger toward the company and may try to solve the perceived imbalance through devaluation of the organization, negative recommendations (word-of-mouth) to other applicants and even stop purchasing the organization’s or brand’s products or services (Gilliland, 1993).
Selection Fairness Theory (Gilliland, 1993) states that procedural and distributive justice rules influence applicant reactions. Procedural justice pertains to the perceived fairness
of the procedures or methods applied when making certain decisions (Folger & Greenberg, 1985). Distributive justice pertains to the perceived fairness of the outcomes or consequences of these decisions (Folger & Greenberg, 1985). Whereas procedural justice is especially related to attitudes about specific processes, distributive justice relates more to attitudes about particular outcomes (Lind & Tyler, 1988). In other words, in a selection context, distributive fairness is the determination of whether or not an applicant receives the hiring decisions the applicant feels he or she deserves, while procedural fairness is the determination of whether the process preceding this outcome seems fair.
Procedural fairness consists of three components, which are: formal characteristics, explanation, and interpersonal treatment. Flowing out of these three procedural antecedents are ten procedural rules. The ten procedural rules all pertain to overall perceptions of the selection process fairness. A few of the most important procedural fairness rules are job relatedness, treatment during the selection procedure and two-way communication. Job relatedness concerns the extent to which an applicant either perceives the measure to be relevant to the job situation or perceives it as valid. Treatment refers to the respect and friendliness with which an applicant was treated (e.g., by the interviewer or test
administrator). Two-way communication entails the extent to which the applicant is given opportunity to ask questions and present himself (Bauer, Truxillo, Sanchez, Craig, Ferrara & Campion, 2001).
In line with Gilliland’s (1993) expectation, Bauer et al. (1998) found that procedural fairness perceptions positively influenced applicants’ evaluations of organizations and
intentions toward organizations. Research has also found relationships between perceptions of procedures (e.g., job-relatedness) and job acceptance intentions and recommendation
intentions (Ryan & Ployhart, 2000). Other studies have also found that there is a relationship between distributive fairness (the fairness of the selection outcome an applicant receives) and
organizational attractiveness (Schinkel et al., 2013; Schinkel et al., 2016). 2.6 Organizational attractiveness
Organizational attractiveness in general is defined as "an attitude or expressed general affect toward an organization and toward viewing the organization as a desirable entity with which to initiate some relationship" (Aiman-Smith, Bauer & Cable, 2001, p. 4). So,
organizational attractiveness refers to the perceived attractiveness of the organization in the eyes of its stakeholders (i.e., consumers, applicants, etc.).
In line with this, according to Aaker (1996), there are ten brand equity dimensions and one of them is organizational associations. Organizational associations refer to the
associations people have with the organization that lies behind the brand. Organizational associations play a particularly crucial role in the case of similarity between attributes of brands or products of competing organizations. These associations show that a brand is more than just its products and services. They are also about the visibility, credibility, and image of the organization. One of the important associations people make is the extent of the
organization’s concern for customers and other stakeholders, such as (potential or rejected) applicants. Organizational attractiveness in selection research (as defined by Bauer et al., 1998 and Ryan & Ployhart, 2000) resembles Aakers’s definition of organizational associations.
According to Bauer et al. (1998) organizational attractiveness is an important determinant in the maximization of selection utility. Research has proven that considerable economic loss can occur as a result of top candidates perceiving another organization as more attractive and therefore turning down a job offer at an organization that is being perceived as less attractive (Murphy, 1986).
Brand attitude and brand image seem similar to organizational attractiveness in the applicant reactions research (as developed by Bauer et al., 1998; Ryan & Ployhart, 2000).
Further, Chang & Liu (2009) found that brand attitude and brand image are factors for brand equity. Therefore, in line with this, we expect that organizational attractiveness will have a positive effect on brand equity. Brand equity will be discussed in more detail in the next paragraph.
2.7 Brand equity
The concept of brand equity and its relations to employer branding and corporate branding will be further explained below, through the use of existing literature on this topic. Brand equity in general refers to those marketing effects that can be uniquely attributed to the brand (Keller, 1993). According to Yoo and Donthu (2001, p. 1) the brand equity construct refers to “the incremental utility or value added to a product by its product name.”
Occasionally, brand equity is confused with brand image. According to Biel (1992), brand equity represents value, while brand image represents the associations consumers might have with a certain brand. Two types of brand equity exist, namely customer based and employer based brand equity.
Customer based brand equity refers to the effect of consumer’s knowledge of a certain brand and their response to the marketing of the brand or product (Keller, 1993). Customer-based brand equity thus pertains to a certain state in which a consumer is familiar with a certain brand and recalls several favorable, strong, and unique associations of this particular brand. So, “customer-based brand equity is the differential effect of brand knowledge on consumer responses to the marketing of the brand.” (Keller, 1993, p. 2.) This brand knowledge consists of two components: 1) brand awareness and 2) brand image, which consists of a set of brand associations. In addition, Yoo and Donthu (2001, p. 1) define consumer-based brand equity as “consumers' different responses between a focal brand and an unbranded product when both have the same level of marketing stimuli and product attributes.” The differential response of consumers regarding similar products of competing
organizations may be attributed to the brand name and clearly represent the outcomes of the long-term marketing efforts invested into the brand.
According to Keller (1993), the foundation of customer-based brand equity is consumer memory. When an individual starts to think of the need for a new product of a certain brand, specific information most strongly linked to the specific brand will come to mind. Examples of such information are price, word- of-mouth, a consumer’s past experience with it, and other types of information (Pitta & Katsanis, 1995). This means that information about how the brand or organization treats applicants may also influence brand equity. Knowledge acquired through research concerning customer-based brand equity may contribute to understanding how to develop and implement an employer brand that spreads messages to prospective employees about the quality of an organization as an employer (Wilden et al., 2010).
Employer based brand equity can be defined as “the effect of brand knowledge on potential and existing employees of the firm” (Backhaus & Tikoo, 2004, p.504). The degree to which the brand is a factor in the retention of existing and attraction of potential employees adds up to the equity related with the employer brand. Employer brand equity motivates potential applicants to apply (Backhaus & Tikoo, 2004). Employer brand investments seem to affect potential employer’s attractiveness and consequently its employee-based brand equity. This form of brand equity is continuously becoming more recognized among financial markets, and human capital is becoming an increasingly more important part of an
organization’s market value (Cairncross, 2000). However, even though there is increasing competition within employment markets, little research has investigated the procedures through which potential employees evaluate potential employers, and the employee-based brand equity deep-rooted in these potential employer evaluations (Ewing, Pitt, de Bussy & Berthon, 2002; Sutherland, Torricelli & Karg, 2002).
2.8 Recommendation intentions
Changes in the previously discussed variables may have certain consequences. An example of such a consequence is recommendation intentions. Therefore, recommendation intentions will be further explained in order to show how it is connected to recruitment outcomes and employer branding.
Recommendation intentions pertain to the intentions to recommend the organization to other applicants (Bauer et al., 1998; Gilliland, 1994; Smither, Reilly, Millsap, Pearlman & Stoffey, 1993). Several studies have found that recommendation intentions are influenced by selection fairness (Ryan & Ployhart, 2000; Schinkel et al., 2013; 2016; see also Hausknecht et al, 2004). For example, Ryan and Ployhart (2000) found that fair procedures have a very strong influence on applicants’ intentions to recommend the organization to others.
Furthermore, recommendation intentions are related to organizational attractiveness (Bauer et al., 1998; Schinkel et al., 2013; 2016). In the article of Hausknecht et al. (2004) the
correlation between organizational attractiveness and recommendation intentions is .57. Because organizational attractiveness and brand equity seem related, it may be expected that brand equity is also related to recommendation intentions.
According to Rynes (1993), an organization’s total number of applicants may expand if applicants mention positive information to other potential employees after the selection experience. Furthermore, recommendation intentions can be perceived as a part of word-of-mouth (Ryan & Ployhart, 2000). Word-of-word-of-mouth in a recruitment context pertains to
interpersonal communication about an organization as an employer or about specific jobs and is independent of the organization’s recruitment activities (Van Hoye & Lievens, 2004). For example, word-of-mouth can entail conversations with friends or advice from college professors. It can contain both positive and negative information, and by definition comes from an external source. Job seekers find out about job openings through various sources, for
example through advertising, job fairs, and job websites. But another important source for job seekers is frequently consulting family members, friends, and other people about
employment information. (Breaugh, 2013; Van Hoye, Weijters, Lievens & Stockman, 2016). In line with this, Collins and Stevens (2002) have investigated the effects of word-of-mouth as a recruitment source. These studies indicate that word-of-mouth can be an influential source of employment information affecting important job search and recruitment outcomes (for a review, see Van Hoye, 2014). In line with these developments, organizations aim to exploit the power of word-of-mouth in recruitment and explore ways in which it might be used in the most effective manner (Van Hoye et al., 2016).
Furthermore, previous experience is found to influence the trust that potential
employees place in the firm’s employer and customer brands (Wilden et al., 2010). Therefore, it is crucial that employers align the different brand messages that are sent out by the various departments within the company (e.g., marketing and HR department). Finally, word-of-mouth by means of referrals seems to be the most reliable source regarding employer brand information and managers must utilize this fact by implementing employee referral programs (Wilden et al., 2010).
2.9 Purchase intentions
Chang & Liu (2009, p.1690) describe purchase intentions as “a costumer’s plan to buy a specific brand.” In the context of this proposal, purchase intentions will be applied to
whether applicants will still consider purchasing a product or service of an organization after being rejected. Aaker (1996) mentioned a possible relationship between brand equity and important stakeholder outcomes such as purchase intentions. In addition, Cobb-Walgren, Ruble and Donthu (1995) measured the effect of brand equity on purchase intentions through the implementation of two studies (high and low risk: hotels vs. household cleansers) and found that higher brand equity increases purchase intentions. Also, Chang & Liu (2009) found
a cycle in which higher brand equity increases brand preference, which in turn increases purchase intentions. However, they did not study a direct positive effect of brand equity on purchase intentions, but with brand preference as a mediator between these two variables. Unlike Chang and Liu (2009), Jalilvand, Samiei and Mahdavinia (2011) did study the direct relationship between brand equity and purchase intentions. In line with Cobb-Walgren et al. (1995) they found evidence that a higher degree of brand equity results in higher purchase intentions. In the current thesis the main focus lies on the direct influence of brand equity on purchase intentions.
2.10 Research question
This paper aims to investigate whether the degree of consensus information has an influence on rejected applicants’ reactions. Furthermore, this paper will investigate the relationship between rejected applicants’ selection fairness perceptions and organizational attractiveness and subsequent recommendation intentions and purchase intentions. Finally, this paper aims to explore the influence of brand equity in this process. What influence does consensus information have on perceived selection fairness? What influence does perceived selection fairness have on a) recommendation intentions, and b) purchase intentions?
Subsequently, what role do organizational attractiveness and brand equity play in this process?
2.11 Hypotheses
The first hypothesis tests whether the provision of consensus information influences rejected applicants’ perceptions of selection fairness. Ployhart et al. (2005) found that providing rejected applicants (students applying to universities) with consensus information (“many other candidates were also rejected”) appeared to have a positive effect on these rejected applicants’ fairness perceptions. In line with these findings, we expect consensus information to have a positive effect on selection fairness perceptions.
H1: The degree of consensus information in a rejection e-mail will have an influence on perceived distributive selection fairness, such that information stressing high consensus will lead to higher fairness perceptions.
Further, Ployhart et al. (2005) found that providing rejected applicants with high consensus information appeared to have a positive influence on these rejected applicants’ organizational attractiveness perceptions. Therefore, we also presume high consensus information to have a positive influence on perceived organizational attractiveness.
H2: The degree of consensus information in a rejection e-mail will have an influence on perceived organizational attractiveness, such that information stressing high consensus will lead to higher perceived organizational attractiveness.
Further, several studies have found (Bauer et al., 1998; Schinkel et al., 2013; 2016) that fairness perceptions have a positive influence on organizational attractiveness. In line with these findings, we also expect that when applicants have higher selection fairness perceptions, this will lead to increased perceived organizational attractiveness.
H3: Perceived distributive selection fairness will have a positive influence on perceived organizational attractiveness.
To our knowledge, no research has been done on the effect of organizational attractiveness perceptions on brand equity. However, Aaker (1996) has explored the relationship between organizational associations and brand equity and found a significant
positive relationship between organizational associations and brand equity. As organizational associations seem similar to organizational attractiveness, we expect organizational
attractiveness to be positively related to brand equity.
H4: Perceived organizational attractiveness will have a positive influence on brand equity.
Several studies have found that organizational attractiveness has a positive effect on recommendation intentions (Bauer et al., 1998; Hausknecht et al., 2004; Schinkel et al., 2013; 2016). Because organizational attractiveness and brand equity seem related, it may be
expected that brand equity is also related to recommendation intentions.
H5: Brand equity will have a positive influence on recommendation intentions.
Finally, several studies (Chang & Liu, 2009; Cobb-Walgren et al., 1995; Jalilvand et al., 2011) have found that higher brand equity increases purchase intentions. Because these various studies have found this relationship to be confirmed, we also expect that brand equity will have a positive effect on purchase intentions within the context of rejected applicants.
2.12 Conceptual model of relationships
Consensus
information Perceived selection fairness
Perceived organizational
attractiveness Brand equity
Recommendation intentions Recommendation intentions Purchase intentions Purchase intentions H1 H2 H3 H5 H4 Corporate Branding Employer Branding H6 3. Method
This chapter describes the empirical part of the pre-test and main study in this thesis. First, the primary characteristics of the collected samples will be explained. Next, the variables in the studies will be discussed. Finally, a brief description will be given of the analytical approach that was used for the studies. See the appendix for the complete questionnaire.
3.1 Pre-test study
3.1.1 Research design and procedure of pre-test study
A pre-test study was performed in order to investigate whether differences in provision of consensus information (included versus excluded) were apparent to respondents. In
addition, we tested whether differences occurred in respondents’ reactions to the two rejection e-mail templates. Based on a previous study concerning the effects of consensus information by Ployhart et al. (2005), consensus information in a rejection email was varied between two groups of respondents (including vs. excluding consensus information). This entailed a
same hypothetical email, with one exception. The control group consisted of respondents receiving the standard email. This email contained the following sentence: “Unfortunately, we did not find a match between our requirements and your resume. Therefore you will not be invited for a selection interview.” (i.e., excluding consensus information). In contrast, the experimental group consisted of applicants receiving an adapted (presumably improved) version of the standard rejection email. This email contained the following sentence instead: “Unfortunately, there were many applicants for this particular vacancy. Among them were applicants with a resume that was more suitable with our requirements.” (i.e., including consensus information). (For the exact text of the email see Appendix 1).
The questionnaire necessary for this pre-test study was developed in Qualtrics. This pre-test study was performed among friends, family members and acquaintances through the use of personal email addresses and Facebook.
3.1.2 Sample of pre-test study
The primary aim of the pre-test study data collection was to reach as many rejected applicants as possible, with a minimum sample of forty. Here it was kept in mind that the response rate might be around fifty per cent. Therefore, eighty possible respondents were approached for this study. The sample for the pre-test consisted of family, friends and acquaintances on Facebook and email. They were all approached through mail (Facebook mail and private mail). Of the eighty applicants that were invited to participate in filling out the questionnaire, 45 actually filled out the questionnaire (56.25%). Six of these respondents did not fill out sufficient answers concerning the main variables and were therefore deleted. Of the 39 respondents who completely filled out the questionnaire, five did not fill out demographic information. Twenty-four of the remaining 34 respondents were female (70.59%); mean age was 33.32 (standard deviation = 6.44; age range = 24-55), mean work experience in years was 9.35 (SD = 6.80), 94.12% of respondents reported to currently be
employed. 30.30% of the respondents who participated in the questionnaire had an applied university level education and 48.48% had a university level education.
3.1.3 Measures of pre-test study
The variables were measured mainly through existing questionnaires. The answers were measured by means of a 5-point Likert scale, such that higher numbers indicate higher agreement. The demographic variables that were collected consisted of age, gender, education level, work experience, and current work situation (employed or unemployed) (as suggested by Saunders & Lewis, 2012).
To measure respondents’ need for more (consensus) information in the email, one item based on Ployhart et al. (2005) was used (“I would like to receive more information about my rejection”).
To measure the distributive selection fairness, two items developed by Bauer et al. (2001) were used (e.g., “I think the fact that I was rejected is unfair”). The Cronbach’s alpha in the pre-test was .68. The correlation between the two items was r = .52.
Perceived organizational attractiveness was measured with four items from the scale developed by Ployhart and Ryan (1998) (e.g., “I find the organization where I applied for a job attractive”). Originally, this variable was measured with five items, but deleting one item greatly increased the alpha (in line with Ployhart & Ryan, 1998). The Cronbach’s alpha in this study was .89.
Brand equity was measured with one item developed for this study, based on Chang & Liu (2009) and Jalilvand et al. (2011): “ The brand (company x) in my view is trustworthy”). The item for brand equity was first translated from English to Dutch and back translated from Dutch to English in order to increase reliability.
Recommendation intentions were measured with one item developed by (Ryan & Ployhart, 1998) (“I intend to recommend the organization with which I applied for a job to
other job seekers”).
Purchase intentions were measured with one item, which was developed for this study based on the recommendation intention item. (“I intend to purchase products from the
organization or brand with which I applied for a job at this specific organization”). 3.2 Main study
3.2.1 Research design and procedure of the main study
This thesis will describe an explanatory study through a quantitative research design because there has already been done some research regarding certain aspects of the topic. The aim of the main study was to investigate whether applicants would react differently to being rejected with or without receiving consensus information. This was tested with a quasi-experimental field study in a Dutch insurance company. This entails a comparison between two groups of applicants. Based on Ployhart et al. (2005), consensus information in a rejection email was varied between two groups of respondents (excluding vs. including
consensus information). Questionnaires were collected at one point in time, i.e. post-rejection. All applicants who uploaded their resume and were rejected for the job offer were invited to participate by an invitation email. Both groups received the exact same email, with one
exception. The control group consisted of applicants receiving a standard rejection email from the company. This email contained the following sentence: “Unfortunately, we did not find a match between our requirements and your resume. Therefore you will not be invited for a selection interview.” (i.e., excluding consensus information). In contrast, the experimental group consisted of applicants receiving an adapted (improved) version of the standard rejection email. This email contained the following sentence: “Unfortunately, there were many applicants for this particular vacancy. Among them were applicants with a resume that was more suitable with our requirements.” (i.e., including consensus information). (For the exact text of the email see Appendix 1).
The rejection email also contained the request and link to the online data collection tool (Qualtrics) regarding the questionnaire. There was also a description of the research in order to invite applicants to participate in filling out the questionnaire. The voluntary nature of participation was emphasized and the confidentiality of all respondents’ answers was guaranteed. Participants could win gift certificates as a reward and a total of eight gift certificates were handed out after the project was finalized (chosen by lottery). Participants were asked to provide their email address if they wished to participate in the lottery. 3.2.2 Sample of main study
The primary aim was to reach as many rejected applicants as possible, with a minimum sample of a hundred. Here it was kept in mind that the response rate might be around fifty per cent. Therefore, the approached number of respondents that were invited was 200. The sample consisted of Dutch participants applying for a variety of jobs on a part-time or fulltime base at a Dutch insurance company.
Of the 264 applicants that were invited to participate in filling out the questionnaire, 149 actually filled out the questionnaire (56.44%). Seven of these applicants did not fill out sufficient answers concerning the main variables and were therefore deleted. Of the 142 job applicants who completely filled out the questionnaire, 58 were male (40.8%); mean age was 43.34 (standard deviation = 11.63; age range = 20-63), mean work experience in years was 18.37 (SD = 11.35), 42.10% of respondents reported to currently be employed. 50.8% of the rejected applicants who participated in the questionnaire had an applied university level education and 27.8% had a university level education; 95% of the rejected applicants who participated in the questionnaire had previous application experience; 69.4% of the rejected applicants who participated also replied to other applications from other companies; 24.3% of the rejected applicants who participated also had products such as an insurance from the company.
3.2.3 Measures of main study
All measures in the main study were similar to those of the pre-test study, with one exception: consensus information was not measured in the main study as consensus information in the main study was manipulated into two different types of e-mails.
Distributive selection fairness: The Cronbach’s alpha of this study was .71. The correlation between the two items was r = .56.
Perceived organizational attractiveness: similar to the pre-test, this variable was first measured with five items, but deleting one item increased the alpha (in line with Ployhart & Ryan, 1998). The Cronbach’s alpha in this study was .93. (For more information, see Table 2).
4. Results
Below, the results of the pre-test study will be described first, followed by the results of the main study. For both studies, first the correlation matrix will be discussed.
Subsequently, the results from the analyses will be outlined. 4.1 Pre-test study
4.1.1 Correlation analysis pre-test
The means, standard deviations, and correlations of the study variables are provided in Table 1. A first observation derived from the table is that organizational attractiveness, brand equity, recommendation intentions and purchase intentions are all highly related.
Table 1.
Means, Standard Deviations, Correlations, Reliability Coefficients Pre-test
Variables M SD 1 2 3 4 5 Distr. fairness 2.68 .71 (.68) Org. attr. 2.21 .92 .27 (.89) Brand equity 2.77 .93 .16 .68** (-) Recomm. int. 2.39 1.18 .40* .57** .52** (-) Purchase Int. 2.73 1.15 .29 .58** .63** .75** (-)
** Correlation is significant at the 0.01 level (2-tailed).
4.1.2 Analyses pre-test study
The pre-test was designed to investigate whether differences in the consensus
information between the two emails (included versus excluded) were obvious to respondents. In addition, it was tested whether differences in respondent reactions occurred between the two rejection e-mail conditions. The manipulation entailed the following: in the experimental group, respondents received a fictional rejection email including the text “There were many applicants for this particular vacancy. Among them were applicants with a resume that was more suitable with our requirements.” (i.e., including consensus information). In the control group, respondents received the exact same rejection email, but with the (originally used) text “Based on your resume we did not find a match. Therefore you will not be invited for a
selection interview.” (i.e., excluding consensus information). We expected that people receiving the rejection email including consensus information would report less need for information than those receiving the email without consensus information. This question was investigated with an independent t-test. This model was significant: t(38) = 2.24; p < .05. This means that on average respondents who received the email including consensus information
reported a significantly lower need for information (M = 3.47) than respondents who received the e-mail excluding consensus information (M = 4.24). The fact that respondents receiving the rejection email including consensus information expressed a lower need for information than those receiving the email excluding this information, confirms that the consensus information manipulation has worked.
Additionally, we also investigated whether differences occurred between the two email groups for all main variables in the model (that is, in reactions to the rejection emails). All questions were investigated with independent t-tests.
First, distributive fairness did not differ significantly between the two e-mail groups: t(37) = -.60; p = ns. This means that on average respondents who receive the e-mail excluding consensus information (M = 2.63) did not regard the outcome as significantly less fair than the respondents who received the email including consensus information (M = 2.77).
Second, perceived organizational attractiveness did not differ significantly between the two e-mail groups: t(37) = -.96; p = ns. However, the pattern was in the expected direction: on average, respondents who received the email including consensus information perceived the organization as more attractive (M = 2.38) than the respondents who received the email without consensus information (M = 2.09).
Third, brand equity also did not differ significantly between the two e-mail groups: t(37) = -.87; p = ns. However, again the pattern was in the expected direction: on average, respondents who received the email including consensus information perceived brand equity as higher (M = 2.93) than the respondents who received the email without consensus
information (M = 2.67).
Fourth, recommendation intentions did not differ significantly between the two groups: t(36) = .72; p = ns. However, again the pattern was in the expected direction: on average respondents who received the e-mail excluding consensus information (M = 2.50) did
not significantly differ from the respondents who received the email including consensus information (M = 2.21).
Finally, purchase intentions did not differ significantly between the two e-mail groups: t(35) = .65; p = ns. However, again the pattern was in the expected direction: on average respondents who received the e-mail excluding consensus information (M = 2.83) did not significantly differ from the respondents who received the email including consensus information (M = 2.57).
4.2 Main study
Below, first the correlation matrix for the main study, (see Table 2) will be discussed. Subsequently, the results from the regression analyses will be outlined. Finally, the results of additional mediation analyses will be discussed.
4.2.1 Correlation analysis main study
The means, standard deviations, and correlations of the study variables are provided in Table 2. A first observation derived from the table is that organizational attractiveness, brand equity, recommendation intentions and purchase intentions are all highly related, which is similar to the pre-test findings.
Table 2.
Means, Standard Deviations, Correlations, Reliability Coefficients Main Study
Variables M SD 1 2 3 4 5 Distr. fairness 2.69 .80 (.71) Org. attr. 3.56 1.01 .06 (.93) Brand equity 3.72 1.03 .01 .68** (-) Recomm. int. 3.00 .99 .22** .67** .55** (-) Purchase int. 2.89 .89 .04 .50** .37** .49** (-)
** Correlation is significant at the 0.01 level (2-tailed).
4.2.2 Hypotheses testing
The first hypothesis proposed that a rejection e-mail including consensus information would have a positive influence on distributive selection fairness. This hypothesis was tested with an independent t-test. This model was not significant: t(139) = .92; p = ns. This means that on average respondents who received the e-mail excluding consensus information (M = 2.74) did not significantly differ from the respondents who received the email including consensus information (M = 2.62). Thus, consensus information did not have a significant influence on distributive selection fairness, meaning that the first hypothesis was not supported.
The second hypothesis proposed that a rejection e-mail including consensus
information would have a positive influence on perceived organizational attractiveness. This hypothesis was also tested with an independent ttest. This model was significant: t(139) = -1.96; p = .05. On average, respondents who received the e-mail excluding consensus information (M = 3.44) reported significantly lower organizational attractiveness than the respondents who received the email including consensus information (M = 3.76). Thus, as
expected, consensus information did have a significant influence on perceived organizational attractiveness, meaning that the second hypothesis was supported.
The third hypothesis proposed that distributive fairness would have a positive
influence on perceived organizational attractiveness. This hypothesis was tested with multiple regression. This model was not significant: F(1, 141) = .43; p = ns (R = .06; R2 = .00; ΔR2 = .00; distributive fairness: B = .07; β = .06). This means that distributive selection fairness does not influence perceived organizational attractiveness. Thus, the third hypothesis was not supported.
The fourth hypothesis proposed that perceived organizational attractiveness would have a positive influence on brand equity. This hypothesis was also tested with multiple regression. This model was significant: F(1, 141) = 119.65; p < .001 (R = .68; R2 = .46; ΔR2 = .46; perceived organizational attractiveness: B = .69; β = .68). This means that, as
expected, perceived organizational attractiveness significantly influenced brand equity, with higher organizational attractiveness leading to higher brand equity. Thus, the fourth
hypothesis was supported.
The fifth hypothesis proposed that brand equity would have a positive influence on recommendation intentions. This hypothesis was tested with hierarchical multiple regression. In the first step, education level was controlled for because this had appeared to correlate with recommendation intentions. This first model was significant: F(1, 125) = 8.76; p < .01. In the second step, brand equity was entered. This second model was also significant: F(2, 125) = 35.01; p < .001 (R = .60; R2 = .36; ΔR2 = .30; education: B = -.17; β = -.18; brand equity: B = .53; β = .55).This means that brand equity significantly influences recommendation intentions, even when controlling for education level. In other words, when rejected applicants perceive higher brand equity, they are more inclined to recommend the
The sixth hypothesis proposed that brand equity would likewise have a positive influence on purchase intentions. The hypothesis was tested with multiple regression. This model was again significant: F(1, 138) = 21.42; p < .001 (R = .37; R2 = .14; ΔR2 = .14; brand equity: B = .32; β = .37). This means that brand equity significantly influences purchase intentions: when rejected applicants perceive higher brand equity, they are more inclined to keep purchasing the organization’s products or services. Thus, the sixth hypothesis was supported.
4.2.3 Additional mediation analyses
In addition to the analyses performed in relation to the hypotheses, three additional mediation analyses were conducted in order to investigate whether some of the proposed relationships concerned mediated relationships. For this purpose, the Process macro was used as developed by Hayes (2012). The results were as follows.
Fairness as mediator of the relationship between consensus information and organizational attractiveness
First, it was investigated whether the relationship between consensus information and organizational attractiveness was mediated by distributive fairness. The effect of consensus information on distributive fairness a
1 = - .12 means that two applicants that differ by one unit
on consensus information are estimated to differ by 0.12 units on fairness. The sign of is a
1 is
negative, meaning that those receiving the rejection email including consensus information (as opposed to the standard email) are estimated to be lower in their fairness perceptions. However, this effect was statistically not significant (t = -.92; p = ns). The effect b
1 = .09
indicates that two applicants who receive the same consensus information (the same email) but that differ by one unit in their level of fairness are estimated to differ by b
1 = 0.09 units in
perceived organizational attractiveness. The sign of b
relatively higher in fairness are estimated to be higher in organizational attractiveness. However, this effect likewise was statistically not significant (t = .91; p = ns).
Further, the total effect of consensus information on organizational attractiveness is c = .33, meaning that two applicants who differ by one unit in consensus information are estimated to differ by 0.33 units in their reported organizational attractiveness. The positive sign means that the person receiving the email including consensus information reports higher organizational attractiveness. However, this effect was only marginally significant (t = 1.96; p =.052).
Finally, the indirect effect of -.01 means that two applicants who differ by one unit in consensus information are estimated to differ by -0.01 units in their reported organizational attractiveness as a result of the tendency for those who receive the standard email to perceive the outcome as less fair, which in turn translates into lower organizational attractiveness. However, this indirect effect is statistically not different from 0, as reviewed by a 95% BC bootstrap confidence interval that is not different from 0 (-.10 to .01). This means that the relationship between consensus information and organizational attractiveness was not mediated by distributive fairness. (For more details see Table 3).
Table 3.
Results for distributive fairness as a mediator of the relationship between consensus information and perceived organizational attractiveness
Consequent
Fairness (M) Org. attractiveness (Y)
Antecedent Coeff. SE p Coeff. SE p
Consensus info (X) a 1 -.12 .14 ns c1’ .34 .17 <.05 Fairness (M) --- --- --- b1 .09 .10 ns Constant i1 2.74 .09 <.001 i2 3.18 .31 <.001 R2 = .01 R2 = .03 F(1,139) = .84; p = ns F(2,138) = 2.33; p = ns
Effect SE P LLCI ULCI
Direct effect c1’ .34 .17 <.05
Total effect c1 .33 .17 <.10*
Boot SE Boot LLCI Boot
ULCI
Indirect effect a1 b1 -.01 .02 -.10 .01
*Note: p = .052
Brand equity as mediator of the relationship between organizational attractiveness and recommendation intentions
Second, it was investigated whether the relationship between organizational
attractiveness and recommendation intentions was mediated by brand equity. The effect of perceived organizational attractiveness on brand equity a = .70 means that two applicants
that differ by one unit on consensus information are estimated to differ by 0.70 units on fairness. The sign of a
1is positive, meaning that those relatively higher in organizational
perceptions are estimated to be higher in brand equity. This effect was statistically significant (t = 11.15; p = .000). The effect b
1 = .16 indicates that two applicants who experience the
same level of organizational attractiveness but that differ by one unit in their level of brand equity are estimated to differ by b
1 = 0.16 units in their intentions to recommend the
organization to others. The sign of b
1 is positive, meaning that those relatively higher in brand
equity are estimated to be higher in their recommendation intentions. However, this effect was only marginally significant (t = 1.93; p = .056).
Further, the total effect of perceived organizational attractiveness on recommendation intentions is c = .65, meaning that two applicants who differ by one unit in perceived
organizational attractiveness are estimated to differ by 0.65 units in their reported recommendation intentions. The positive sign means that the person reporting higher
organizational attractiveness also reports higher recommendation intentions. This effect was significant (t = 10.41; p = .000).
Finally, the indirect effect of .11 means that two applicants who differ by one unit in perceived organizational attractiveness are estimated to differ by 0.11 units in their reported recommendation intentions as a result of the tendency for those who perceive the organization as more attractive to report higher brand equity, which in turn translates into higher
recommendation intentions. This indirect effect is not significant as reviewed by a 95% BC bootstrap confidence interval that is not different from 0 (-.04 to .24). This means that the relationship between organizational attractiveness and recommendation intentions was not mediated by brand equity. (For more details see Table 4).
Table 4.
Results for brand equity as a mediator of the relationship between perceived organizational attractiveness and recommendation intentions
Consequent
Brand equity (M) Recomm. Intentions (Y)
Antecedent Coeff. SE p Coeff. SE p
Org. attract. (X) a1 .70 .06 <.001 c1’ .53 .08 <.001
Brand equity (M) --- --- --- b1 .16 .08 <.10*
Constant i1 1.22 .23 <.001 i2 .52 .25 <.05
R2 = .48 R2 = .46
F(1,137) = 124.29; p <.001 F(2,136) = 57.15; p <.001
Effect SE p LLCI ULCI
Direct effect c1’ .53 .08 <.001
Total effect c1 .65 .06 <.001
Boot SE Boot LLCI Boot
ULCI
Indirect effect a1 b1 .11 .07 -.04 .24
*Note: p = .056
Brand equity as mediator of the relationship between organizational attractiveness and purchase intentions
Third, it was investigated whether the relationship between organizational
attractiveness and purchase intentions was mediated by brand equity. As was found for the previous mediation analysis, the effect of perceived organizational attractiveness on brand
equity a
1 = .70 means that two applicants that differ by one unit on consensus information are
estimated to differ by 0.70 units on fairness. The sign of a
1 is positive, meaning that those
relatively higher in organizational perceptions are estimated to be higher in brand equity. This effect was statistically significant (t = 11.15; p = .000). The effect b
1 = .03 indicates that two
applicants who experience the same level of organizational attractiveness but that differ by one unit in their level of brand equity are estimated to differ by b
1 = 0.03 units in their
purchase intentions. The sign of b1 is positive, meaning that those relatively higher in brand
equity are estimated to be higher in purchase intentions. However, this effect was statistically not significant (t = .39; p = ns).
Further, the total effect of perceived organizational attractiveness on purchase intentions is c = .44, meaning that two applicants who differ by one unit in perceived organizational attractiveness are estimated to differ by 0.44 units in their reported purchase intentions. The positive sign means that the person reporting higher perceived organizational attractiveness also reports higher purchase intentions. This effect was significant (t = 6.81; p < .001).
Finally, the indirect effect of .02 means that two applicants who differ by one unit in perceived organizational attractiveness are estimated to differ by 0.02 units in their reported purchase intentions as a result of the tendency for those who perceive the organization as more attractive to report higher brand equity, which in turn translates into higher purchase intentions. This indirect effect is not significant, as reviewed by a 95% BC bootstrap
confidence interval that is not different from 0 (-.14 to .16). This means that the relationship between organizational attractiveness and purchase intentions was not mediated by brand equity. (For more details see Table 5).
Table 5.
Results for brand equity as a mediator of the relationship between perceived organizational attractiveness and purchase intentions
Consequent
Brand equity (M) Purchase intentions (Y)
Antecedent Coeff. SE p Coeff. SE p
Org. attr. (X) a1 .70 .06 <.001 c1’ .42 .09 <.001 Brand equity (M) --- --- --- b1 .03 .09 ns Constant i1 1.22 .23 <.001 i2 1.29 .26 <.001 R2 = .48 R2 = .25 F(1,137) = 124.29; p < .001 F(2,136) = 23.10; p <.001
Effect SE p LLCI ULCI
Direct effect c1’ .42 .09 <.001
Total effect c1 .44 .06 <.001
Boot SE Boot LLCI Boot
ULCI
Indirect effect a1 b1 .02 .07 -.14 .16
5. Discussion
Below, the most noticeable findings of the present study will be discussed. Subsequently, the limitations concerning the present study will be described. Finally, implications and suggestions for further research and practice will be provided.
The results of the present study mainly supported the proposed hypotheses. For instance, it was expected that the degree of consensus information in a rejection e-mail (i.e.,
in the rejection message, an applicant receives the information that many other applicants have also been rejected) would have an influence on perceived organizational attractiveness, such that information stressing high consensus would lead to higher perceived organizational attractiveness. As expected, it was found that the degree of consensus information had a significant influence on perceived organizational attractiveness. This means that respondents who received the rejection e-mail excluding consensus information reported significantly lower organizational attractiveness than the respondents who received the rejection email including consensus information. This finding is in line with previous results by Ployhart et al. (2005). They found that providing rejected applicants (students applying to universities) with consensus information appeared to have a positive effect on these rejected applicants’ organizational attractiveness perceptions.
Further, it was found that, as expected, perceived organizational attractiveness subsequently had a positive influence on brand equity. This means that, perceived
organizational attractiveness significantly influenced brand equity, with higher organizational attractiveness leading to higher brand equity. This finding is in line with research into brand equity: Aaker (1996) found a positive relationship between organizational associations and brand equity. Furthermore, Chang & Liu (2009) demonstrated that brand equity depends on brand attitude and brand image, which seem very similar to organizational attractiveness as developed by Ryan & Ployhart (2000).
Also, we presumed brand equity to subsequently have a positive influence on recommendation intentions. Indeed, it was found that brand equity significantly influenced recommendation intentions. In other words, when brand equity perceptions were higher, applicants reported to be more inclined to recommend the organization to others. This is in line with previous studies in the field of applicant reactions, that found perceived
