Employer Branding: The effect of the perception of innovativeness of the brand, location, and industry on job choice

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Employer Branding: The effect of the perception of

innovativeness of the brand, location, and industry on job

choice

Author: Maurice Nijmeijer Student number: S2539535 Email: j.m.m.nijmeijer.1@student.rug.nl

Supervisor: Dr. F. Eggers Co-assessor: Dr. J.E.M. van Nierop

Faculty of Economics and Business University of Groningen

Duisenberg Building, Nettelbosje 2, 9747 AE Groningen, The Netherlands P.O. Box 800, 9700 AV Groningen, The Netherlands

http://www.rug.nl/feb

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1 Introduction

Understanding why people choose for a certain employer is of importance to both the job seeker and the employer. The job seeker is interested because the choice influences that person’s life. A satisfying job increases this person’s life satisfaction (Rice, McFarlin, Hunt, & Near, 1985). Furthermore, the job becomes part of the employees resume. The employer needs to understand this because it needs to attract personnel suited for the job, which enhances company performance (Bakker & Schaufeli, 2008). It could be challenging to attract qualified employees. Increased competition amongst competing companies on the labor market could be an example. The research mentioned above leads to the content of this paper.

There may be various reasons for choosing a job. Research has identified that the employer, and particularly the employer brand, is an important factor in job choice. In addition, this research takes location, industry, salary, image and awareness of the brand into account. This research can potentially beneficial for employers. Knowledge about the value of the employer brand and the other variables may help attracting qualified employees. Which in turn increase the performance of the company.

based brand equity is linked to job choice, which is not done before. Employee-based brand equity is defined as ‘the differential effect that brand knowledge has on an employee’s response to their work environment’ (King & Grace, 2009). The research regarding employee-based brand equity is trying to understand what value is added to the brand by the employee. This value comes from employee efforts and their work for the brand, this should be managed by the firm.

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(Calantone, Cavusgil, & Zhao, 2002). The fact that firms need to be innovative to stay in business makes firms that are perceived as such interesting to work for. This relation may be stronger for job seekers that have an innovative personality. The perception of innovativeness of both the employer brand and the job by employees is important for job choice. The customer view on innovativeness implies that the more the products has perceived benefits, the higher the perception of innovativeness (Lowe & Alpert, 2015). This view could be transferred into an employee-based view. This would imply that more benefits, as perceived by the employee provided by the brand, will lead to higher perceived innovativeness.

Innovativeness does not only apply to the employer brand. Innovativeness can also be linked to industries or locations. Both the industry and the location can be perceived as innovative by the employee. Different industries and locations can have various reputations. According to Pianigiani (2009), in the USA, consumer products industry has the best reputation. Whereas the energy and the financial industry do not receive a good score. Moreover, the location of the firm can be rated differently. The location of the headquarters of a company determines reputation (The Nielsen Company, 2015).

In past research, location, industry and brands are confounded. Employers are perceived as a bundle of employer brand, industry and location. A challenge in this research is to treat these variables as correlating variables. In the past, for example: the headquarter of ING bank in the financial services industry which is located in Amsterdam is treated as one single variable. This study tries to treat these variables as separate. However, it is not realistic to completely separate them. Employers are located in a city and operate in a certain industry. Therefore, to separate the effects of the employer brand, industry and location, two choice-based conjoint experiments are used.

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and costly. A strong employer brand value may result in employees accepting lower pay or retention at equal pay.

The main question this paper is: How can job choice be explained by the perception of innovativeness of the employer brand, location and industry.

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2 Literature review and hypotheses

In this part literature is reviewed and from this literature hypotheses are formulated. Table 1 shows previous literature on variables contained in this study. This study needs to separate the location and industry effects from the employer brand effect in order to measure the image (innovativeness) effects. Every variable will be related to innovativeness since that is the main image that is used in this study. The table provides an overview of what will be reviewed and which elements are included in this study. Two studies focus on Employee-based brand equity, eight studies discuss brand image, industry is discussed two times and location three times, innovativeness is discussed three times and salary is focused on four times. The table shows that this study is unique in its approach to explain job choice. The image of innovativeness is used as a common denominator in this study.

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2.1 Employee-based brand equity

Employee-based brand equity is a form of brand equity that is similar to the concept of customer-based brand equity. The difference between these two concepts is that the focus of the analysis is on the customer or the employee. Customer-based brand equity is defined as: ‘the differential effect of brand knowledge on consumer response to the marketing of the brand’ (Keller, 1993). In this research the focus is on the employee.

To find suitable candidates a firm should market itself in such a way that it becomes interesting for job seekers (Keller, 1993). Employer brand equity needs to be managed properly in order to raise its value. i.e. an image of innovativeness could be built to attract candidates. A higher employee-based brand equity can result into higher employee satisfaction, intentions to stay and positive word of mouth of employees (King & Grace, 2010). Employee-based brand equity is formed by the perception of the employees about that brand. In turn, the employer should see the employee as a stakeholder. In order to build a better brand equity a firm needs to create employer value proposition. This consists of what the firm does to be perceived as positive for employees (Pattnaik & Misra, 2017). This view must be positive in order for that person to choose for this firm.

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2.2 Employer brand image

Employer brand image is defined as the general impression of a certain brand. It should be acknowledged that the employer brand image is not just one entity. It comprises of five different identities: actual, perceived, communicated, ideal and desired image (Lievens, Van Hoye, & Anseel, 2007). Before a job seeker or any person is able to form an opinion about a brand he or she needs to be familiar with the brand. The mere exposure effect can explain brand awareness. When a person is familiar with a certain brand this person is also very likely to have a preference for this brand (Bornstein & D’agostino, 1992). After this awareness is established an opinion about this company can be formed in the brain of a job seeker. Keller (2001) has found that different dimensions are used to build a brand image. On a higher level these attributes can be viewed as employer brand awareness and employer brand image as perceived by the employee. When a brand image is perceived as strong, job seekers will view job opportunities more positive measured by salary compared to weaker brands (DelVecchio, Jarvis, Klink, & Dineen, 2007). The employer brand image can be used as an asset for the firm. The company vision, organizational culture and formal policies create an internal image formed by the firms’ employees. This internal image is the source of the external image which is transmitted as the overall image of the employer (Dowling, 1993). A positive employer brand image leads to higher performance of companies (Maxham, Netemeyer, & Lichtenstein, 2008). If there is perceived congruence between employer and employee values it is likely a job seeker will apply for a job at this employer (Cable & Judge, 1996; Lievens & Highhouse, 2003). A positive image of an employer starts a virtuous circle: A strong employer image attracts good employees, which in turn result in higher firm performance, which leads to a good word of mouth by customers, this results in best applicants for jobs (Ambler & Barrow, 1996).

The general image of an employer is important to job seekers. After being aware of the brand an opinion about the employer is formed. In this research, innovativeness is the central image that is used. Innovativeness will be discussed in a following section. This leads to the following hypothesis:

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2.3 Industry

The industry in which the job is offered is of importance in job choice. In certain industries a particular education or skills are required. The job seeker must obtain or have these abilities to be able to work in the industry. Industry is defined as the enterprises that are present within a certain field or economy which is viewed as a collective. The industry that one will choose to work in is for a part dependent on how this industry is perceived (Gatewood, Gowan, & Lautenschlager, 1993). A good reputation and a higher appeal of an industry will most likely result in more job seekers applying for a job within that industry. E.g. the pharmaceutical industry has a different image compared to the media industry image. Research has shown that the corporate brand image is correlated with the industry image (Burmann, Schaefer, & Maloney, 2008).

2.4 Location

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2.5 Innovativeness

The employer image in this study is the image of innovativeness. In the following part the innovativeness of the brand, industry, location and personality are discussed.

2.5.1 Brand

Having an innovative employer brand contributes to the overall performance of the firm (Hult, Hurley, & Knight, 2004) Innovative firms may attract candidates that perceive innovativeness as an important factor to choose a job or firm. An innovative image of the employer brand can be formed through opinions of referent others. The opinions of referent others form the attitudes, beliefs and actions of individuals, known as the social influence theory (Kelman, 1958). The individual may or may not attribute an innovative image to an employer brand. Innovativeness is seen as an important attribute for assessing the firms’ attractiveness as employer. This leads to the following hypothesis:

H2: An innovative employer brand will have a positive effect on job choice

2.5.2 Industry

The perception of innovativeness is formed by what kind of product or service or employer brands are present in the industry. If this activity is considered as innovative the related industry will be perceived as innovative as result of that perception. By making use of the theory of the halo effect, if the brands active in the industry have a reputation for being innovative the industry will be perceived as innovative. This leads to the following hypothesis:

H3: An innovative industry will have a positive effect on job choice

2.5.3 Location

The perception of innovativeness of the location can be explained by clustering theory. Clustering theory refers to the spatial movement towards a certain place if similar firms are located there, e.g. Silicon Valley (Malmberg & Maskell, 2002; Porter & Stern, 2001). Firms in a similar location are most likely similar to other firms active in that area. If those other firms are perceived as innovative a job seeker will classify all firms from that area as innovative and therefore see the location as innovative. When the firms that operate in a location are perceived as innovative, the location is perceived as innovative. This leads to the following hypothesis:

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2.5.4 Personality

A person that has or perceives him or herself as innovative may be more likely to feel connected to employer brands, locations and industries they perceive as innovative. A good person-organization fit key for job satisfaction (O’Reilly III, Chatman, & Caldwell, 1991). Job seekers are more likely to choose a job when there is harmony between their personal values and the perceived values of the employer, there must be a fit between person and organization (Cable & Judge, 1996). This leads to the following hypotheses:

H5a: The effect of location innovativeness is stronger when a person has an innovative personality

H5b: The effect of perceived industry innovativeness is stronger when a person has an innovative personality

H5c: The effect of a perceived innovative employer brand is stronger when a person has an innovative personality

2.6 Salary

This measure is an annual amount of money received by an employee. Salary is the compensation one receives for working for a certain employer. Salary is part of the job choice process since an employee has certain expectations of monetary rewards for doing a certain job. According to previous research, salary is the most important attribute influencing job choice (Saks, Wiesner, & Summers, 1996). The salary expectations of the employee should match the offer made by the firm in order for them to choose this job (Cable & Judge, 1996). When pay is not high enough this may be a reason not to choose this job. The first form of selection is a search for a particular level of pay (Chapman, Uggerslev, Carroll, Piasentin, & Jones, 2005). From this can be inferred that a higher salary will most likely result in job choice. However, employees are willing to work for a weaker brand if the salary is of a certain higher level (Saks et al., 1996). This leads to the following hypothesis:

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2.7 Conceptual model

Following from the literature review the following conceptual model is constructed. The model shows the researched relations to job choice and how these relationships are moderated. The grey area represents experiment 1, without the mentioning of a brand. The box around the variables represents experiment 2 including the brand. The experiments are explained in the next section.

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3 Methodology

The central question in this paper is: How can job choice be explained by the value of employee-based brand equity and brand and job innovativeness and awareness associations. In answering this question different variables, as discussed in previous parts, need to be taken into account to find how job choice can be explained. A bottom-up approach is taken to answer this question. Different systems are cognitively combined to create output. In this research innovativeness is the central image that is used to explain job choice. The image of innovativeness is expected to be attractive to job seekers.

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3.1 Experiment 1

The first conjoint study will have a head-hunter scenario which means that the participant only sees a salary, location and an industry in the choice set. In table 2 is shown in an example what the participant sees in experiment 1.

Table 2: Example choice set experiment 1

3.2 Experiment 2

The second choice based conjoint will include an employer brand, salary, industry and location. In table 3 is shown in an example what the participant sees in experiment 2. The logos of the companies are depicted instead of the just the name.

Table 3: Example choice set experiment 2

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brand. Salary is differing from -15% to +15% of the amount the respondent reported as expected salary. For employer brand, location and industry it is clear that a part-worth method should be used. The method for salary; linear, quadratic or part-worth, will be determined during analysis.

Table 4: Overview of attributes and levels

To be able to analyse both experiments jointly, a link must be established to combine them. The link is found in the location and the industry, these two variables combined can only correspond with one employer. An employer is fixed to a single location and industry and can therefore be recovered. On this basis both experiments can be combined. However, it needs to be investigated if combining both experiments does not change the betas of location and industry. The result can be found in table 6.

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effects of personal work values on job decisions. In this study is hypothesised that a person that has an innovative personality will be more likely to choose a job which they perceive as innovative. This variable will be indicated on a 5-point Likert scale. Likert scales are very easy to use and are understandable for respondents.

Before a person can have an image of an employer, awareness of that employer brand needs to be established (Moisescu, 2009). Equation (3) will for this test include a moderation between awareness and perceived innovativeness of the employer brand. If the models are not significantly differ from each other, both awareness and innovativeness of the employer brand have their own individual effect on job choice.

3.4 Modelling approach

The results of a choice based conjoint analysis will provide betas per variable of which the total sum of a combination of variables will give the overall utility. Different combination of variables will result into a different utility per choice. The gammas in the model denote the variables that are measured separate from the choice based conjoint analysis. For the conjoint part two different approaches are taken to see if the employer brand effect is measured separately from the location and industry effect. The full model with all employer brands, locations and industries is compared to a model which does not include the employer brands.

The utility formula of the benchmark model (1) is as follows:

𝑉 = 𝛽_%&'(+ 𝛽_*+,+ 𝛽_-.+ + 𝛽_/012343%+ 𝛽₅₆ + 𝛽_73103830+ 𝛽_9:++ 𝛽_;(<;(< + 𝛽_623=>%( + 𝛽₆₀₃₌₍+ 𝛽_+?(0 + 𝛽_.@322 + 𝛽_A&0810B + 𝛽_C(0DB((ED+ 𝛽_623=>%(01=D

+ 𝛽603%BF + 𝛽7&B?3+ 𝛽-<D>3%E&<+ 𝛽/>%3=@>+ 𝛽G(>>3%E&< + 𝛽.&2&%F

Extending the previous model with constructs for employer brand innovativeness, innovativeness of the location, innovativeness of the industry and awareness the extended model (2) becomes.

𝑉 = 𝛽_%&'(+ 𝛽_*+,+ 𝛽_-.+ + 𝛽_/012343%+ 𝛽₅₆+ 𝛽_73103830+ 𝛽_9:+ + 𝛽_;(<;(< + 𝛽_623=>%(+ 𝛽₆₀₃₌₍+ 𝛽_+?(0+ 𝛽_.@322 + 𝛽_A&0810B + 𝛽_C(0DB((ED

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When extending the model with moderation effects for an innovative personality the interaction model (3) becomes:

𝑉 = 𝛽_%&'(+ 𝛽_*+,+ 𝛽_-.+ + 𝛽_/012343%+ 𝛽₅₆ + 𝛽_73103830+ 𝛽_9:+ + 𝛽_;(<;(< + 𝛽_623=>%( + 𝛽603=(+ 𝛽+?(0 + 𝛽.@322 + 𝛽A&0810B + 𝛽C(0DB((ED+ 𝛽623=>%(01=D

+ 𝛽603%BF + 𝛽7&B?3+ 𝛽-<D>3%E&<+ 𝛽/>%3=@>+ 𝛽G(>>3%E&< + 𝛽.&2&%F

+ 𝛾_A%&0E*00(4+ 𝛾_{I(=&>1(0*00(4}+ 𝛾_{*0E?D>%F*00(4}+ 𝛾_-J&%303DD + 𝜂_{A%&0E*00(4,:3%D(0&21>F}𝐵𝑟𝑎𝑛𝑑 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠 ∗ 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑡𝑦 + 𝜂_{I(=&>1(0,:3%D(0&21>F}𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠 ∗ 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑡𝑦 + 𝜂*0E?D>%F,:3%D(0&21>F𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠 ∗ 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑡𝑦

4 Results

4.1 Sample

The survey is built on an online platform in order for participants to participate online. The sample consists of all possible people within or even just outside our personal network. The study is conducted in English only, we assume all the participants have a sufficient level of English to complete the survey. Participants are from 24 different countries of which the majority are Dutch (n=130; 53,9%) and Chinese people (n=76; 31,5%). The other countries represented account for less than 2% of the entire sample. Of all participants 123 are male and 116 are female, 2 participants preferred not to mention their gender. The fast majority of the sample is aged between 18 and 30 years old (83,4%), 31 to 50 years (9,1%) and 51 to 70 (7,5%). 59% has at one point lived close to one of the cities used in the survey. 155 respondents are students and 86 respondents are not students. 51% of the sample has a bachelor’s degree and 29% a master title. In total 241 respondents completed the survey. 91 respondents left the survey without completing it.

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between the different variables and next to that there are no multicollinearity issues that should be taken into consideration.

In table 5 below is depicted how often a brand, industry or location is perceived as innovative. In general, can be said that brands are not seen as innovative by many participants. Unilever, Heineken and TomTom are perceived as innovative by more than 25% of the participants. Surprisingly, Electrolight, the non-existent control brand, is seen as more innovative than Nuon, DE and Rabobank. Surpising is that Electrolight is perceived as innovative by more respondents than respondents that are aware of its existence. Amsterdam and Rotterdam are perceived as more innovative compared to The Hague and Utrecht. Only the banking & financial services industry is not often seen as innovative compared to the other industries in the sample. All other employers used are mostly known to the respondents in the sample.

Table 5: Descriptives Innovativeness & Awareness

Brand Innovative Percentage Awareness Percentage

Rabobank 18 7,5% 175 73,2% ING 51 21,2% 190 79,5% ASN Bank 30 12,4% 128 53,6% Unilever 85 35,3% 205 85,8% DE 25 10,4% 156 65,3% Heineken 70 29,0% 194 81,2% KPN 55 22,8% 169 70,7% TomTom 61 25,3% 157 65,7% Electrolight 28 11,6% 15 6,3% Eneco 45 18,7% 139 58,2% Nuon 26 10,8% 134 56,1% Shell 52 21,6% 210 87,9% Location The Hague 52 21,6% Amsterdam 136 56,4% Utrecht 67 27,8% Rotterdam 105 43,6% Industry

Banking & Financial services 52 21,6%

Consumer goods 99 41,1%

Electronics 143 59,3%

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4.2 Combining experiments

In the model with the employer brand and in the head hunter scenario the betas of industry and location are comparable. In the brand scenario model is clear that the importance of location is not as important as in the other two models. The results show that one complete model is able to measure the employer brand effects from the industry and location effects. The values of the industry and location do not differ much in both models as can be seen in table 6.

Table 6: Combining models comparison

model Full Hunter Head scenario Brand Industry

Banking & Financial services 0.0746 0.0793 0.0629

Consumer goods 0.3641* 0.3841* 0.4863* Electronics -0.3573* -0.3795* -0.3559 Energy -0.0804* -0.0839 -0.1933 Location The Hague -0.1674* -0.1795* -0.0112 Amsterdam 0.1394* 0.1260* 0.0279 Utrecht 0.1475* 0.1576* 0.0191 Rotterdam -0.1195 -0.1041* -0.0358 *=significant at 0.05 confidence

Table 7: Relative importance of attributes Relative importance Benchmark model (1) model (2) Extended

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In the extended model is clear that both the innovativeness of the brand and the industry have an important share. Relatively they are considered more important than the location.

4.3 Model fit

The three models as described above are compared in table 8. This table shows that on all measures the interaction model is the strongest model. The McFadden R2 is used to compare fit of the models. There is not much difference between the extended and the interaction model. This implies that moderation effects in the interaction model are only slightly improving the total model. Since nested models are used, a likelihood ratio test (LR-test) can be performed. This result indicates that the models are significantly different from each other. Model (1) is significantly different from (2) and (3), model (2) is significantly different form (3). Next to that information criteria can be used to compare the models. The Akaike Information Criterion shows the lowest value for the interaction model showing a better quality compared to the other models. From above can be concluded that the interaction model is the best model. From table 9 can be seen that on average the model is 8,64% wrong in predicting the actual choices.

Table 8: Model comparison

Benchmark model (1) Extended model (2) Interaction model (3)

Loglikelihood -4288.7 -4187.4 -4180.7

McFadden R2 7,54% 9,73% 9,87%

AIC 8615.4 8420.8 8413,4

LR-test (p-value) 0.000 0.003

Table 9: Goodness of fit of the model

Option 1 Option 2 Option 3 Option 4

Observed shares 22,90% 25,82% 25,46% 25,82%

Predicted shares 40,16% 23,01% 19,66% 17,15%

Absolute error 17,26% 2,81% 5,80% 8,67%

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4.4 Conjoint models

A multinomial logit is tested on the aggregate level using a maximum likelihood procedure. The results of the benchmark model (1), which does not include measures for awareness and the innovativeness of the employer brand, location and industry. These are included in the extended model (2). The reported values are the betas and gammas found in the multinomial logit test. The betas for Shell, the energy sector and the city or Rotterdam are recovered separately since these corresponding variables are effect coded. The results are reported in table 10.

In the benchmark model (1) Unilever is the most desired employer. In the extended model (2) Unilever is no longer the most desired, Shell is seen as the most desired, it provides the highest utility. Nuon is the least preferred employer, the utility has the most negative value. Interesting is the result for the benchmark company Electrolight. This is a non-existent company and it still is higher preferred, even though the value is not significant, compared to other employers in the extended model. Many of the employer betas are not significant. However, the effect size influences the utility levels. In the extended model the significant beta of Unilever seems to be taken over by the constructs that were not tested in the benchmark model.

The industry for consumer goods is the most attractive whereas the electronics industry is the least attractive. On the aggregate level, individuals receive most utility from working in the consumer goods industry. The betas for location are quite stable in both models. It seems that the effects of location are not at all affected by changes in the model. Utrecht is the most preferred location followed by Amsterdam, Rotterdam to the least preferred location The Hague.

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the most positive influence on the utility, this is followed by the innovativeness of the employer brand and the awareness of the employer brand.

4.5 Moderation analysis

To account for the moderation whether a person sees him or herself as an innovative person, the model is extended further with interaction effects as described in equation (3). The results of the model including interactions is reported in table 9. From the table only one significant result can be found. This suggest proof for hypothesis H5b. When a person considers him or herself as innovative the effect between an innovative industry and job choice is significantly stronger. There is no evidence to support hypotheses H5a and H5c. The estimate for innovativeness of the employer brand is close to zero and for location innovativeness it is almost zero and negative.

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Table 10: Model comparison

Brand

Benchmark model (1) Estimates Extended model (2) Estimates Interaction model (3) Estimates Rabobank 0.0174 0.0322 0.0270 ING -0.0171 -0.0870 -0.0864 Asn -0.0575 -0.0016 -0.0012 Unilever 0.3112* 0.1202 0.1261 DE -0.0865 -0.0827 -0.0900 Heineken 0.1080 0.1441 0.1468 KPN 0.0996 0.0687 0.0681 TomTom -0.0498 -0.0903 -0.0919 Electrolight -0.1195 0.1128 0.1082 Eneco -0.1190 -0.1013 -0.0974 Nuon -0.4304* -0.3675* -0.3664* Shell 0.3437* 0.2513* 0.2570* Industry

Banking & Financial services 0.0746 0.1984* 0.2063*

Consumer goods 0.3641* 0.3988* 0.3922* Electronics -0.3573* -0.4746* -0.4783* Energy -0.0804* -0.1226 -0.1203 Location The Hague -0.1674* -0.1549* -0.1559* Amsterdam 0.1394* 0.1210* 0.1211* Utrecht 0.1475* 0.1649* 0.1687* Rotterdam -0.1195 -0.1310 -0.1339 Salary 4.8422* 4.9724* 4.9609* None option 5.0202* 5.5194* 5.5094* Innovativeness of Brand 0.5364* 0.3912 Location -0.1309* -0.0455 Industry 0.5633* 0.0003 Awareness 0.2929* 0.2922* Interactions

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5 Discussion

From the analysis can be concluded that some employer brands, industries and locations are more preferred than others. Which is in line with what one would expect. Different people have different preferences. The main question in this thesis is: How can job choice be

explained by the perception of innovativeness of the employer brand, location and industry.

There is support for the hypothesis that the employer brand and the industry are perceived as innovative. The perception of innovativeness of the location is not significant and the effect is negative. Next to these main results the other results will be discussed in detail.

The location of the company is of less importance to the job seeker. In the analysis is clear that a perceived innovative location results in a negative response to job choice. This implies that job seekers in the sample do not attribute value to an innovative location. It was expected that this relation would be positive. It was expected that a location would be perceived as positive when the employer in that location was perceived as innovative. This effect is more negative when a person has an innovative personality. The moderation effect is smaller than the main effect of location. This may be caused by a more positive feeling towards an innovative location than is true on the aggregate level. The relative importance of location is considered low, this may be an explanation for the negative estimate. However, 56,4% and 43,6% of the respondents have respectively rated Amsterdam and Rotterdam as having an innovative perception. The fact that the estimate still comes out negative may be due to the fact that job seekers are willing to travel to any place.

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Some employers in the sample are more desired compared to others. This desirability follows from; first brand awareness and after a positive image. When the employer is perceived as innovative the relation towards job choice is positive. This relationship is slightly stronger when a person also has an innovative personality, however the effect is not significant. The coefficient is positive however it is close to zero. The reason for choosing a certain employer may be dependent on if the job seeker perceives a fit between the employer and him or herself. This fit is personal and can be more than just an image of innovativeness. In general, the percentages of participants that rated employers as innovative is low. Unilever has the highest perception of innovativeness (35,3%). When it comes to relative importance of employer brand, after salary and industry it is the third most important attribute. This implies that job seekers do care about which firm they will work for.

Comparing the benchmark model (1) and the extended model (2), the attractiveness of each brand is increased when this brand is perceived as innovative. This is also true when only accounting for the perception of innovative industry. Employers and industries are more attractive if they have an image of innovativeness. The image of innovativeness is appealing to job seekers. It results in job seekers preferring the jobs at these employers and in these industries more compared to non-innovative competitors. The non-existent brand, Electrolight, is more preferred compared to known and existing brands. It may be that companies that have a neutral image are more preferred than known employers that have a negative image.

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seeker that feels connected to innovativeness is more likely to choose these types of firms. However, it may be that just a fit between the organisation and the job seeker that makes an employer more attractive. The fit between the organisation and the employee may increase the retention of employees. Next to that the image of the industry is an important attribute for the job seeker. It may be interesting for employers in the same industry to work together to improve the image of their industry. The employer brands in a particular industry can reap more benefits if not just their brand image is positive.

6 Limitations & future research

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7 Summary

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8 Bibliography

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9 Additional chapter: Job content image

Next to the study above the image of the job content is researched. During the research this part has been taken out due to a lack of fit towards the goal of this study. However, the research that has been done is reported below to provide insights in the effects of this variable explaining job choice. This could be used in a future study.

9.1 Job image

With job image associations is meant the content of the job as perceived by the job seeker. The same job can be containing different responsibilities and freedoms in different firms. From previous literature different attributes have been determined e.g. (Cable & Judge, 1996; DelVecchio et al., 2007; Dowling, 1993; Maxham et al., 2008). From these studies the following attributes have been found as most prominent when it comes to job choice: job demands, autonomy, social contacts, skill development and possibilities of growth. These attributes have been all put together in the Job Content Questionnaire (Karasek et al., 1998). In this study the focus will be placed on the job content attributes that are related to innovativeness:

- Autonomy: Freedom and control as perceived by the employee

- Creativity: The employee can do their job as they think is the best way

- Routine: There is no fixed routine in their job, employees do not have a fixed schedule - Time management: Employees receive sufficient time to explore different

perspectives.

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In the conceptual model there would be a moderating effect of job image between brand innovativeness and job choice.

9.2 Methods

The questions in the survey asked ‘To what extent do you consider the following attributes to be part a job in which innovativeness is key’. They are measured on a 5 point Likert scale.

- Autonomy - Creativity - Routine

- Time management

Job image is a moderation effect which is investigated. As hypothesised: if the job image is perceived as innovative the effect between employer brand innovativeness and job choice is stronger. The image is measured on four different dimensions. Factor analysis will try to reduce the dimensions into one variable in order to simplify the modelling approach.

Before the interactions are tested, a factor analysis (principal component, no rotation) is performed to create one job image construct from 4 related questions in the survey. From theory it is already proven that these variables all indicate an innovative job image. The results indicate that job image can be measured on one scale. A Cronbach’s alpha of 0,729 is found. The KMO test provides a significant result (0.000), the eigenvalue of creating one component is 2.26 which can also be concluded from the location of the ‘elbow’ in the scree plot. The values are saved and used in the moderation analysis.

Including this interaction to formula (3):

(4) 𝑉 = 𝛽%&'(+ 𝛽*+, + 𝛽-.++ 𝛽/012343% + 𝛽56+ 𝛽73103830 + 𝛽9:++ 𝛽;(<;(<+

𝛽_623=>%(+ 𝛽₆₀₃₌₍+ 𝛽_+?(0 + 𝛽_.@322 + 𝛽_A&0810B + 𝛽_C(0DB((ED+ 𝛽_623=>%(01=D + 𝛽_603%BF+ 𝛽_7&B?3+ 𝛽_-<D>3%E&< + 𝛽_/>%3=@>+ 𝛽_G(>>3%E&<+ 𝛽_.&2&%F+ 𝛾_A%&0E*00(4 +

𝛾_{I(=&>1(0*00(4}+ 𝛾_{*0E?D>%F*00(4} + 𝜂_{A%&0E*00(4,`('*<&B3} 𝐵𝑟𝑎𝑛𝑑𝐼𝑛𝑛𝑜𝑣 ∗ 𝐽𝑜𝑏𝐼𝑚𝑎𝑔𝑒 + 𝜂A%&0E*00(4,:3%D(0&21>F𝐵𝑟𝑎𝑛𝑑𝐼𝑛𝑛𝑜𝑣 ∗ 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑡𝑦 + 𝜂I(=&>1(0,:3%D(0&21>F 𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛 ∗

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9.3 Results

There is a positive eta for the interaction for job image and employer innovativeness and innovativeness of the industry. This implies the effect becomes stronger (0.090, P-value 0.332), however it is not a significant effect. The perception of job image seems to be not that important to a job seeker.

9.4 Future research

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10 Appendix 2: R-script

rm(list = ls()) #clear workspace

dat <- read.csv("Employer_Branding_CBC.csv") dat <-dat[!(dat$X9_salary == 0),]

# convert data

dat <- mlogit.data(dat, choice="Selection_Dummy", shape="long", alt.var="Alternative_id") # calculate models

ml1<- mlogit(Selection_Dummy ~ Employer.Rabo + Employer.ING + Employer.asn + Employer.Unilever + Employer.DE + Employer.Heineken +

Employer.KPN + Employer.Tomtom + Employer.EL + Employer.Eneco + Employer.Nuon +

Industry.Banking + Industry.Consumer + Industry.Electronics + Location.The.Hague + Location.Amsterdam + Location.Utrecht + Annual.Salary.val + None_option | 0, dat)

summary(ml1)

# Recover reference levels

Employer.Shell <- (-1) * sum(coef(ml1)[1:11]) Industry.Energy <- (-1) * sum(coef(ml1)[12:14]) Location.Rotterdam <- (-1) * sum(coef(ml1)[15:17]) # recover standard errors and ref.level std error covMatrix <- vcov(ml1)

Employer.Shell.Std.Error <- sqrt(sum(covMatrix[1:11, 1:11])) Employer.Shell.zValue <- Employer.Shell / Employer.Shell.Std.Error Industry.Energy.Std.Error <- sqrt(sum(covMatrix[1:11, 1:11])) Industry.Energy.zValue <- Industry.Energy / Industry.Energy.Std.Error Location.Rotterdam.Std.Error <- sqrt(sum(covMatrix[1:11, 1:11]))

Location.Rotterdam.zValue <- Location.Rotterdam / Location.Rotterdam.Std.Error # WTP

coef(ml1)[1:11]/coef(ml1)["Annual.Salary.val"] Employer.Shell/coef(ml1)["Annual.Salary.val"] ### Use only first CBC without Employer Brand # convert data

dat1 <- mlogit.data(dat[which(dat$CBC_part == 1),], choice="Selection_Dummy", shape="long", alt.var="Alternative_id")

# calculate models

ml2<- mlogit(Selection_Dummy ~ Industry.Banking + Industry.Consumer + Industry.Electronics + Location.The.Hague + Location.Amsterdam + Location.Utrecht +

Annual.Salary.val + None_option | 0, dat1) summary(ml2)

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Industry.Energy.Std.Error <- sqrt(sum(covMatrix[1:8, 1:8])) Industry.Energy.zValue <- -0.0839 / Industry.Energy.Std.Error Location.Rotterdam.Std.Error <- sqrt(sum(covMatrix[1:8, 1:8])) Location.Rotterdam.zValue <- -0.1041 / Location.Rotterdam.Std.Error ### Use only second CBC without Industry and Location

# convert data

dat2 <- mlogit.data(dat[which(dat$CBC_part == 2),], choice="Selection_Dummy", shape="long", alt.var="Alternative_id")

# calculate models

Annual.Salary.val + None_option | 0, dat2) summary(ml3)

#reference levels ml3 covMatrix <- vcov(ml3)

Employer.Shell.Std.Error <- sqrt(sum(covMatrix[1:13, 1:13])) Employer.Shell.zValue <- 0.0714/ Employer.Shell.Std.Error # compare estimates from ml2 and ml4

ml4<- mlogit(Selection_Dummy ~ Industry.Banking + Industry.Consumer + Industry.Electronics + Location.The.Hague + Location.Amsterdam + Location.Utrecht +

Annual.Salary.val + None_option | 0, dat2) summary(ml4) covMatrix <- vcov(ml4) Industry.Energy.Std.Error <- sqrt(sum(covMatrix[1:8, 1:8])) Industry.Energy.zValue <- --0.1933 / Industry.Energy.Std.Error Location.Rotterdam.Std.Error <- sqrt(sum(covMatrix[1:8, 1:8])) Location.Rotterdam.zValue <--0.0358 / Location.Rotterdam.Std.Error cbind(coef(ml2)[1:6], coef(ml4)[1:6])

### Add covariates Awareness of brand dat$Awareness <- 0

dat$Awareness[which(dat$Employer == 1 & dat$X9_awareness_.img....files.logo_Rabobank.png..img. == 1)] <- 1

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dat$Awareness[which(dat$Employer == 8 & dat$X9_awareness_.img....files.logo_Tomtom.png..img. == 1)] <- 1 dat$Awareness[which(dat$Employer == 9 & dat$X9_awareness_.img....files.logo_EL.png..img. == 1)] <- 1 dat$Awareness[which(dat$Employer == 10 & dat$X9_awareness_.img....files.logo_Eneco.png..img. == 1)] <- 1 dat$Awareness[which(dat$Employer == 11 & dat$X9_awareness_.img....files.logo_Nuon.png..img. == 1)] <- 1 dat$Awareness[which(dat$Employer == 12 & dat$X9_awareness_.img....files.logo_Shell.png..img. == 1)] <- 1 ### add covariate innovativeness of brand

dat$Innovativeness <- 0

dat$Innovativeness[which(dat$Employer == 1 & dat$X18_innovativeness_.img....files.logo_Rabobank.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 2 & dat$X18_innovativeness_.img....files.logo_ING.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 3 & dat$X18_innovativeness_.img....files.logo_asn_bank.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 4 & dat$X18_innovativeness_.img....files.logo_Unilever.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 5 & dat$X18_innovativeness_.img....files.logo_DE.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 6 &

dat$X18_innovativenesss_.img....files.logo_Heineken.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 7 & dat$X18_innovativeness_.img....files.logo_KPN.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 8 & dat$X18_innovativeness_.img....files.logo_Tomtom.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 9 & dat$X18_innovativeness_.img....files.logo_EL.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 10 & dat$X18_innovativeness_.img....files.logo_Eneco.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 11 & dat$X18_innovativeness_.img....files.logo_Nuon.png..img. == 1)] <- 1

dat$Innovativeness[which(dat$Employer == 12 & dat$X18_innovativeness_.img....files.logo_Shell.png..img. == 1)] <- 1

### add covariate innovativeness of location dat$LocationInnov <- 0

dat$LocationInnov[which(dat$Location == 1 & dat$X19_innovativeness.locations_Amsterdam == 1)] <- 1 dat$LocationInnov[which(dat$Location == 2 & dat$X19_innovativeness.locations_Utrecht == 1)] <- 1 dat$LocationInnov[which(dat$Location == 3 & dat$X19_innovativeness.locations_Rotterdam == 1)] <- 1 dat$LocationInnov[which(dat$Location == 4 & dat$X19_innovativeness.locations_The.Hague == 1)] <- 1 ### add covariate innovativeness of industry

dat$IndustryInnov <- 0

dat$IndustryInnov[which(dat$Industry == 1 &

dat$X20_innovativeness.industries_Banking.and.Financial.Services == 1)] <- 1

dat$IndustryInnov[which(dat$Industry == 2 & dat$X20_innovativeness.industries_Consumer.Goods == 1)] <- 1 dat$IndustryInnov[which(dat$Industry == 3 & dat$X20_innovativeness.industries_Electronics == 1)] <- 1 dat$IndustryInnov[which(dat$Industry == 4 & dat$X20_innovativeness.industries_Energy == 1)] <- 1 ## Extended model

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Employer.Unilever + Employer.DE + Employer.Heineken + Employer.KPN + Employer.Tomtom + Employer.EL + Employer.Eneco + Employer.Nuon +

Industry.Banking + Industry.Consumer + Industry.Electronics + Location.The.Hague + Location.Amsterdam + Location.Utrecht + Annual.Salary.val + None_option +

Awareness + Innovativeness + LocationInnov + IndustryInnov | 0, dat) summary(ml5)

# Recover reference levels

Employer.Shell <- (-1) * sum(coef(ml5)[1:11]) Industry.Energy <- (-1) * sum(coef(ml5)[12:14]) Location.Rotterdam <- (-1) * sum(coef(ml5)[15:17]) # recover standard errors and ref.level std error covMatrix <- vcov(ml5)

Location.Rotterdam.zValue <- Location.Rotterdam / Location.Rotterdam.Std.Error

###include factors in dataset (1 factor for job image) and make a new dataframe including the factor score choices <- read_xlsx("Factor.xlsx")

dat3<- merge(dat, choices, by.x="New_id", by.y="New_id")

dat4 <- mlogit.data(dat3, choice="Selection_Dummy", shape="long", alt.var="Alternative_id") ##Interaction effects

Awareness + Innovativeness + LocationInnov + IndustryInnov +

I(IndustryInnov * X21_grid1_Do.you.consider.yourself.to.be.innovative. ) +

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Location.Rotterdam <- (-1) * sum(coef(ml6)[15:17]) # recover standard errors and ref.level std error covMatrix <- vcov(ml6)

Location.Rotterdam.zValue <- Location.Rotterdam / Location.Rotterdam.Std.Error #correlation matrix for all constructs

cordata <- dat4[249:252] round(cor(cordata),2)

## Model with interaction Awareness Innovativenes

Awareness + Innovativeness + LocationInnov + IndustryInnov +I(Innovativeness * Awareness) + I(IndustryInnov * X21_grid1_Do.you.consider.yourself.to.be.innovative. ) +

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Employer Branding: The effect of the perception of innovativeness of the brand, location, and industry on job choice