BYOD The Next Big Thing in Recruiting? Examining the Determinants of BYOD Service Adoption Behavior from the Perspective of Future Employees

Transcription

1 BYOD The Next Big Thing in Recruiting? Examining the Determinants of BYOD Service Adoption Behavior from the Perspective of Future Employees Michael Loose Faculty of Information Management Neu-Ulm University Wileystr. 1, Neu-Ulm Germany Andy Weeger Center for Research on Service Science Neu-Ulm University Wileystr. 1, Neu-Ulm Germany Heiko Gewald Center for Research on Service Science Neu-Ulm University Wileystr. 1, Neu-Ulm Germany ABSTRACT Bring Your Own Device (BYOD) enables employees to use their privately-owned devices for business purposes. There is an ongoing debate on the costs, benefits and potential threats of this concept amongst practitioners. Surprisingly, employees and their expectations and attitudes towards BYOD are rarely part of these discussions. Contributing to this research area, this study answers questions on the determinants of BYOD adoption and acceptance behavior. For that purpose, the UTAUT model was adapted and extended. Quantitative data was collected from students of business and engineering majors in Germany. Performance expectancy was found to be the strongest determinant of behavioral intention to use BYOD services. However, 'perceived threats' a newly introduced construct also showed to have a significant explanatory value. Additionally, the significant impact of behavioral intention to use a BYOD service on employer attractiveness indicates that the offering of BYOD can indeed be a powerful measure to recruit future employees. KEYWORDS Consumerization of IT, Bring Your Own Device, BYOD, UTAUT, Structural Equation Model, Employer Attractiveness Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

2 INTRODUCTION IT departments have invested a lot of effort in building up standardized service offerings for their clients. These efforts have frequently been driven by objectives such as to decrease costs, ensure manageability and IT-security (Vogel et al., 2010). Nowadays, consumer devices such as mobile phones and tablet PCs are evolving faster than ever. Furthermore, business mobility is becoming an essential part of business operations (Harris et al., 2012). In this regard, standardized services offered by the IT department do frequently not cover the requirements imposed on the employees. Additionally, employees want to use the same devices in a corporate environment as they use in their private lives (Holtsnider et al., 2012). Therefore, IT departments are threatened by two trends which are mutually influencing each other: the need for mobile business capabilities and the consumerization of IT (Harris et al., 2012). If these trends could not be sufficiently addressed by corporate IT, firms may find themselves overwhelmed with innovations aimed at the consumer sector spilling over in the corporate environment (Cummings et al., 2009; Ingalsbe et al., 2011). Consumerization of IT refers to the trend that employees want to use the same devices, applications, and IT services in a corporate environment as they use in their private lives (Holtsnider et al., 2012). Throughout this paper, we apply the definition of Niehaves et al. (2012, p. 2) who regard IT consumerization as a scenario in which employees "invest their own resources to buy, learn, and use consumer technology at their workplace". Using consumer devices for business purpose is currently the most visible kind of IT consumerization behavior. Therefore, the current discussion of IT consumerization primarily focusses on the usage of privately-owned computing devices at workplace (Ingalsbe et al., 2011; Holtsnider et al., 2012; Weiß et al., 2012). IT consumerization focusing on devices not on applications and services is commonly referred to as "Bring your own Device" (BYOD), a sub-trend of consumerization. To address the demands of their employees, an increasing number of IT organizations are offering BYOD services to their customers (Györy et al., 2012). Offering a BYOD service implies that firms open their networks and enable data-access to consumer devices. Furthermore, adopting a BYOD service means that employees have to accept policies regarding the usage of their privately-owned devices for business purposes. These policies are established in order to safeguard security and to govern liability (Vogel et al., 2010; Harris, 2012). Subsequently, we define BYOD service as a service offered by corporate IT that allows employees to bring privately owned device to the workplace, to connect them to the corporate network and to use them for business purposes. This type of service offering is subject to an extensive debate among IT executives. These debates often address the question whether enabling employees to use their own devices does outweigh costs and risks. On the one hand, embracing a mobility strategy and integrating consumer devices in the corporate IT infrastructure requires carefully considered investments in security technology (Harris et al., 2012), as well as management and operation concepts (Weiß et al., 2012). On the other hand, anecdotal evidence indicates that there is a high level of motivation to use private devices in business contexts. As they have been born into the digital age in which technology is ubiquitous (Prensky, 2001), especially young employees are willing and sometimes even demanding to use their own devices for business related tasks. Furthermore, the use of privatelyowned devices is difficult to prevent. Therefore, offering a BYOD service could be a reasonable step to prevent the rise of uncontrollable shadow IT infrastructures established by the users (Györy et al., 2012). Although it is expected that consumerization of IT will gain even more momentum in future (Fenn et al., 2011), IS research does not yet provide adequate guidance on this phenomenon (see literature review). As young people, frequently labeled as 'digital natives' (Prensky, 2001), are supposed to be among the first to adopt new information technology, they can be seen as the drivers of this trend. Therefore, the focus of this study has been on students in their final years of study, i.e. the future employees. In order to gain understanding on the factors driving young employees to adopt BYOD services, we postulate the following research question: What are the determinants of BYOD service adoption among future employees? As a shortage of young workers results in a fierce competition in the personal recruitment market (Köchling, 2003), in particular SMEs are looking for ways to remain and become attractive for young employees. Assuming that future employees are likely to adopt BYOD services, we put a second research question forward: Does the attractiveness of a company for future employees increase if they are able and permitted to use their own devices for business purpose? This paper is structured as follows. In the next section the theoretical background is briefly discussed. Drawing on these findings, the development of the research model and the subsequent research methodology is presented. Finally, the results are discussed, conclusions are drawn and suggestions for future research are presented. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

3 LITERATURE REVIEW AND THEORETICAL BACKGROUND Scientific Literature on IT Consumerization and BYOD Although IT consumerization is of great interest for both scholars and practitioners, scientific literature is unexpectedly rare. Analyzing information systems (IS) conference proceedings and journals, only few papers on BYOD appeared. This finding is supported by the structured literature review provided by Niehaves et al. (2012) who identified only 22 scientific papers focusing on consumerization of IT / BYOD. However, most scholars discuss IT consumerization as a result of a reversed technology adoption life cycle, initiated by employees demanding the same ease of use with corporate IT as consumer products offer (Cummings et al., 2009). IT consumerization is usually defined as the dual use of devices, applications, and services for private and business purpose (Ingalsbe et al., 2011; Holtsnider et al., 2012). Harris et al. (2011, p. 2), for instance, define consumerization of IT as the adoption of consumer application, tools, and devices in the workplace. Other authors, such as Niehaves et al. (2012), regard the ownership as key characteristic and conceptualize IT consumerization as a phenomenon in which employees invest their own resources in order to use consumer technology at work. IT consumerization is expected to positively contribute to work performance (Niehaves et al., 2013) and regularly associated with greater autonomy for employees (Niehaves et al., 2012). Additionally, employees perceive that technology which they chose on their own is easier to use while providing increased user experience (Murdoch et al., 2010; Harris et al., 2011). Nonetheless, the phenomenon is also seen critical. In this regard, a structured literature review points out several disadvantages for employees and organizations (Niehaves et al., 2012). Security issues, increasing complexity, loss of control and performance issues are potentially negative effects for organizations. In this regard, Weiß et al. (2012) discuss the impacts on corporate IT at different stages such as information management, information exchange, information systems and information technology. They conclude that the challenges for IT management in organizations increase without a predictable end. Other scholars expect significant cost reductions for organizations due to consumerization (Ingalsbe et al., 2011; Holtsnider et al., 2012). On the employee level, it is supposed that consumerization increases workload while realizing productivity gains (Ingalsbe et al., 2011; Niehaves et al., 2012). As laid out in the introduction, using consumer devices for business purpose is currently the most visible and most discussed kind of IT consumerization behavior. This sub-trend of IT consumerization is commonly referred to as BYOD. We define BYOD as the act of bringing personally owned device to the workplace, connecting them to the corporate network and using them for business purposes. The BYOD concept can be extended into a complete set of BYOD services. A BYOD service is defined as an offering of the IT organization which allows end users to choose and use the devices that best meet their personal and business needs, instead of getting a standardized device from the IT department. Additionally, the BYOD services can be composed of different financial models describing who is actually paying for the device (Bocker et al., 2012). For instance, Choose Your Own Device (CYOD) means that the employee can select a device from a predefined shopping cart which is financed by the company and may also be used for private purposes (Lang, 2012); whereas We Sponsor Your Device (WSYD) implies that the employee receives a financial compensation for using a privately-owned device for business purpose (Vogel et al., 2010). For this study we adopted the following conception of a BYOD service: A BYOD service offered by corporate IT allows employees to use their private and self-financed IT device at work, connect the device to the corporate network, access corporate data and use it for business purposes to the same extent as devices offered by the company. Using a BYOD service requires that the employee accept the terms of use and security policies and that he or she assumes full responsibility for support, installation and maintenance of the respective device(s). Theoretical Foundation: Technology Acceptance To gain knowledge on the factors driving employees to use a BYOD service, this study builds upon prior technology adoption research. IS research provides several theories to explain individual's technology adoption behavior. Most of these theories are based on the premise that human beings are rational and that they consider the implications of their actions before they decide whether to perform a certain action or not. These theories conceptualize intention as a function of beliefs about the likelihood that performing a particular behavior will lead to a specific outcome. The stronger the intention to engage in a behavior, the more likely that the action will be performed (Ajzen, 1991). For instance, the Technology Acceptance Model (TAM) (Davis, 1989) proposes perceived usefulness and perceived ease of use as determinants of an individual's intention to use a technology. These determinants account for positive outcomes associated with using a technology. In the same vein, other technology acceptance models such as the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) hypothesized that the advantages of using a technology and conditions facilitating usage determines an individual s adoption behavior. To decide if technology acceptance models are also suitable for the analysis of service adoption behavior, studies aiming to explain individual s service adoption have been reviewed. These studies indicate that technology acceptance models are capable to explain an individual s adoption behavior in regard to technology based services (Featherman et al., 2003; Amberg Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

4 et al., 2004; Pagani, 2004; Hung et al., 2005; Carlsson et al., 2006; Chen et al., 2007; Mallat et al., 2008). Almost all of the cited studies show that perceived usefulness and ease of use are important determinants of an individual s service adoption behavior. In particular usefulness was found to be the most significant factor (Pagani, 2004; Chen et al., 2007). On the other hand, the role of social influences as a determinant of service adoption was found to be rather mixed. For instance, Carlsson et al. (2006) found no significant impact of social influence on intention. In contrast, the data of Mallat et al. (2008) indicates an impact of social influence on intention. Overall, these studies confirm the capability of technology acceptance models to explain the determinants of service adoption. Nevertheless, Carlsson et al. (2006) as well as Featherman et al. (2003) point out that modifications on existing technology acceptance models are necessary to explain the adoption of mobile services. RESEARCH MODEL As the UTAUT model has been developed through a review and consolidation of the eight most prominent technology acceptance models (Venkatesh et al., 2003), it seems to be suitable to contribute explanations on the factors influencing BYOD service adoption by future employees. Although, the UTAUT such as other technology acceptance models solely focuses on the advantages of using an technology, the usage of innovative technologies must not always be beneficial (Featherman, 2001). To address potential negative outcomes of BYOD service adoption, perceived threats (PT) was included. Furthermore, facilitating conditions which encompasses organizational and technical infrastructure facilitating technology usage was excluded. Excluding this construct is grounded in two reasons. First, Venkatesh et al. (2003) hypothesize facilitating conditions to impact actual usage behavior, which is not examined in this study. Second, as this study focuses on students in their last semesters with relevant work experience (gained in internships and/or practical semesters and since the majority of these students are not employed in a company, they are not able to assess conditions facilitating the dual use of devices for business and private purposes. Finally, employer attractiveness (EA) was included in order to address the second research question. Table 1 provides the definition of all constructs within the model. Table 1: Construct definitions Construct Definition Type Performance Expectancy (PE) Effort Expectancy (EE) Social Influence (SI) Perceived Threats (PT) Perceived Business Threats (PT-B) Perceived Private Threats (PT-P) Employer Attractiveness (EA) The degree to which an individual believes that using the BYOD service will help him or her to attain gains in job performance (Venkatesh et al., 2003). The degree of ease associated with the use of the service (Venkatesh et al., 2003). The degree to which an individual perceives that important others believe he or she should use the service (Venkatesh et al., 2003). The degree to which an individual believes that the use of a BYOD service is associated with threats that are evoking anxious or emotional reactions (Joshi, 1991; Compeau et al., 1999). The degree to which an individual believes that the usage of a BYOD service is threatening his or her job. The degree to which an individual believes that the usage of a BOYD service is threatening his or her private life. The degree to which the attractiveness of a firm for increases if the firm offers a BYOD service. Number of items Reflective 4 Reflective 3 Reflective 3 Formative 8 Formative 4 Formative 4 Reflective 2 The constructs and their proposed relationships are depicted in Figure 1. Assuming that the UTAUT model is already well known in the IS community, only the modifications of the UTAUT model are discussed in detail below. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

5 Perceived Threats Figure 1: Research Model Using an innovation must not always be perceived as beneficial. As the UTAUT model does not cover negative factors inhibiting BYOD service adoption, perceived threats (PT) was added. This construct was derived from Social Cognitive Theory (SCT) (Compeau et al., 1995; Compeau et al., 1999) and major resistance theories in IS literature (Markus, 1983; Joshi, 1991; Cenfetelli, 2004). Building upon the concept of inhibitors of technology adoption and usage, PT is defined as the degree to which an individual believes that the usage of a BYOD service is associated with threats that are evoking anxious or emotional reactions. In analogy to SCT s anxieties, perceived threats are proposed to negatively impact adoption behavior. BYOD is commonly associated with several threats which have the potential to negatively impact adoption behavior. In accordance to Niehaves et al. (2012), these factors can be distinguished between two dimensions: threats addressing the private life and threats addressing business life. Regarding private life, a loss of private data, the retrieval of private data by the company and blurred boundaries between private and business are threats which have been identified by prior research (Niehaves et al., 2012). On the business side, factors such as a loss of business data, causing corruption of the corporate network with malware and violating company policies are seen as threats imposed by using private devices for business purposes (Niehaves et al., 2012). Following these arguments, it is proposed that both dimensions account on the overall threats perceived by an individual. Subsequently, perceived threats was modelled as a second-order construct encompassing perceived business threats (PT-B) and perceived private threats (PT-P). Furthermore, it is hypothesized that overall perceived threats negatively impacts an individual s intention to adopt a BYOD service. Employer Attractiveness From a company's perspective, anecdotal evidence shows that the impact of offering a BYOD service on its attractiveness for actual and future employees is of particular interest. If BYOD service offerings impact an individual s decision-making about their actual or future employers, BYOD services could be used as an argument for both recruiting new employees and retaining actual employees. To examine if a BYOD service offering contributes to the attractiveness of an employer, BYOD influenced employer attractiveness (EA) has been introduced. EA is defined as the degree to which the attractiveness of a firm increases if the firm offers a BYOD service. It is proposed that behavioral intention (BI) and EA correlate positively such as individuals that tend to have a high intention to adopt a BYOD service will rate a company which is offering a BYOD service as an attractive employer. Hence, it is hypothesized that behavioral intention to adopt a BYOD service positively impacts an individual s employer attractiveness evaluations. METHODOLOGY An empirical study among German university students was conducted to measure the effects of the proposed determinants on future employees' intention to use BYOD services. To minimize bias due to different backgrounds, the focal group consists only of students with business and engineering specialization studying in a small German university. Facing a shortage of young engineers and specialized managers (Köchling, 2003), these students are particularly attractive to medium-sized and large industrial enterprises. These companies, in turn, are heavily keen on understanding BYOD service adoption to guide their decisions whether to offer a BYOD service or not. In order to ensure that students can realistically assess BYOD, only students with relevant work experience (due to an internship or practical semester) have been asked to participate. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

6 To retain measurement validity, items were as far as possible taken from prior technology adoption research. In order to relate the items to the BYOD context, the wording of the items was slightly altered. Prior literature constituted the basis for developing adequate measures for construct which have not been employed in prior research (PT and EA). These items have been intensively discussed within the research team to examine whether the measurement items are capable to capture the constructs. Regarding the formative nature of PT, additional tests have been conducted to ensure that all indicators are clearly distinguished and that the construct is captured in all its facets. Table 1 presents the latent variables, their types (reflective or formative), as well as the source and the number of measurement items assigned to each. To collect data, a standardized questionnaire was designed using the measurement items presented in Appendix A. The questionnaire was published by using an online survey tool. A pretest within the target group has been conducted to ensure that the questionnaire understandable and unambiguous. At total, 177 responses have been collected (response rate of approx. 20%). The responses were reviewed to ensure that each respondent completely finalized the survey. As a result of this analysis, we identified 93 responses with at least one missing value (including demographics). After eliminating the missing values, 84 usable responses left, out of which 8 have been provided from visiting international students. The key demographics are depicted in Table 2. Table 2: Demographics (N=84) Gender Age Study Focus Respondents Percentage Male 57 68% Female 27 32% < 18 years 0 0% years 15 18% years 45 54% years 21 25% > 29 years 3 4% Business Focus 22 26% Engineering Focus 45 54% Inter-disciplinary 17 20% DATA ANALYSIS Following structural equation modeling techniques (Chin et al., 2003), SmartPLS (Ringle et al., 2005) was used to model the latent variables and their proposed causal relationships. There were several reasons for using partial leas squarest (PLS). PLS is a well-established algorithm for technology acceptance research (Venkatesh et al., 2003), PLS makes fewer demands on the sample size and it is capable to analyze formative as well as reflective measurement of latent variables (Chin, 1998). Following the recommendations of Hulland (1999), the analysis of the model was carried out in a two stages approach. First the outer model (component measurements) and subsequently the inner model (structural causal paths) has been evaluated. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

7 Table 3: Cross loadings BI EE PE EA SI BI BI BI EE EE EE PE PE PE PE PEA PEA SI SI SI To analyze the component measurements, Average Variance Extracted (AVE), Composite Reliability (CR), Cronbach s Alpha (CA), and Cross loadings were calculated. The cross loadings provided in Table 3 were assessed according to the guidelines of Gefen et al. (2005). All measurement items loaded greater than.707 onto their respective latent variables and were significant at least at the 5% level. The AVE values are also above the recommended threshold of.500 (Fornell et al., 1981). Furthermore, the internal consistency reliability of each reflective measurement item was assessed analyzing CA values, which should be greater than.800 (Nunnally et al., 1994). Only BI and PE do exceed this cut-off value. However, since CA does not take into account that indicators have different loadings and as it is biased against short scales of two or three items, we followed the advice of Chin (1998) and chose CR as an additional measure. All constructs exceed the threshold of.800 which indicates consistency reliability (Nunnally et al., 1994). To assess discriminant validity, the Fornell- Larcker criterion was assessed (1981). This criterion requires a latent variable (LV) to share more variance with the indicators assigned to it than with any other LV. As depicted in Table 5, the square root of the AVE is higher than the LV's correlation with any other LV. Therefore, discriminant validity can be assumed. Table 4: CR, CA and AVE values of the reflective constructs Composite Reliability Cronbach s Alpha Average Variance (CR) (CA) Extracted (AVE) BI EA EE PE SI In contrast to the other latent variables, PT was measured using formative items. Furthermore, the construct includes two meta-facets: the private dimension (PT-P) and the business dimension (PT-B). Therefore PT was modeled as a second order construct, whereas PT-P and PT-B capture both facets of PT. The latent variables PT-P and PT-B are measured by formative indicators. PT itself is captured by the formative indicators associated to the lower order components PT-P and PT-B. This method is based on the recommendations for second-order constructs (repeated indicators approach) provided by Ringle et al. (2012). The indicator weights of the measurement items and the corresponding t-values comply with the limits proposed by Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

8 Chin (1998). In addition, the degree of multicollinearity has been assessed by calculating the variance inflation factor (VIF). All VIF values comply with the threshold proposed by Urbach et al. (2010). B-PT Table 5: Latent variable correlation and Fornell-Larcker criterion PT-B BI EA EE PT-P PE PT SI BI EA EE P-PT PE PT SI The square root of AVE is shown in bold In order to assess the inner model and the hypotheses, PLS path coefficients, their statistical significance and each endogenous LV's coefficient of determination (R²) have calculated 1. Results of the structural model estimation are illustrated in Figure 2Fehler! Verweisquelle konnte nicht gefunden werden.. The significance level is above 0.01 for all path coefficients. In total, the model accounted for a significant amount of variance in BI (R 2 = 60.5%) and EA (R² = 35.2%). Figure 2: PLS structural results DISCUSSION, LIMITATIONS AND FURTHER RESEARCH To understand the factors which drive young employees to adopt a BYOD service and to examine if the intention to adopt a BYOD service is correlated with the attractiveness of companies, a modified UTAUT model was proposed. In contrast to prior UTAUT studies, this study also takes into account potential threats associated with the usage of privately-own devices for business purposes. As the structural results depicted in 1 The PLS algorithms and bootstrapping has been carried out with the following properties: maximum iterations: 300; centroid weighting scheme; individual sign changes; cases: 84; samples: 500. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

9 Figure 2 provide support for almost all hypotheses, this study shows that the proposed research model is capable to explain BYOD service adoption behavior. In addition, a highly significant correlation between the attractiveness of a company for future employees offering the possibility to use privately-owned device and a future employee s intention to adopt a BYOD service was found. Most of the findings regarding the factors determining BYOD service adoption among future employees are in line with prior UTAUT studies. In particular the strong effect of performance expectancies on behavioral intention confirms previous results. Overall, performance expectancies have the strongest influence on behavioral intention (f2 =.337, medium effect). This finding suggests that expectancies regarding gains in job performance due to increased mobility and flexibility are the most important factors leading future employees to adopt BYOD services. Therefore, it could be concluded, that young employees regard the devices they choose and use in private contexts as superior to those provided by corporate IT. On the other hand, future employees also seem to take the sacrifices of BYOD into account. Both, effort expectancies (f2 = 0.081, weak effect) and perceived threats (f2 = 0.137, weak effect) were found to significantly contribute to their behavioural intention. First, the findings show that future employees are aware of the efforts related with using their devices for business purposes (e.g. setting the devices up for work). Second, the results regarding perceived threats indicate that future employees in Germany are aware of the risks associated with BYOD. Furthermore, private and business risk perceptions are pretty much balanced. Hence, it seems as if future employees do not differ between threats related to their private life and threats related to their job. Nevertheless, even the sum of the effects of effort expectancies and perceived threats is weaker than the effect of performance expectancies. This finding indicates that the performance effects expected by future employees outweigh the efforts and the potential threats. These expectations are capable to give an explanation why especially young employees are willing to use their privately-owned device for business purpose even if it is not permitted. In line with Mallat et al. (2008), social influence was also found to significantly contribute to future employees behavioural intention (f2 = 0.162, weak effect). This is an indication that young people identify with the devices they use (Swallow et al., 2005; Lin et al., 2011). Regarding the second research questions, this study shows that the attractiveness of a company for a future employee is positively correlated with his or her intention to adopt a BYOD service. Furthermore, descriptive data reveals that German business and engineering students have a strong tendency to use privately-owned devices for business purposes. Taking these findings into consideration, it can be argued that BYOD services can indeed play a vital role in recruiting these students as future employees. Regarding the strong performance expectancies of young employees associated to BYOD, the dual use of consumer devices could at least at an individual level also contribute to work performance. Descriptive data reveals that business and engineering students in fact have a strong tendency towards BYOD. Subsequently, it can be argued that BYOD services can indeed play a vital role in recruiting these students as future employees. This finding contrasts the data captured in a recent study among 600 senior business and IT leaders in 17 countries. This study found that only 20% of business leaders believe that BYOD services will benefit recruitment and retention efforts (Avanade, 2012). Nevertheless, the study cited above also reports that 32% have already changed policies to make their workplace more appealing to younger employees. As consumerization of IT will gain momentum in future (Fenn et al., 2011), the findings of our study indicates that the effect of BYOD as a recruiting tool should not be underestimated. Regarding implications for practice, our study encourages CIOs to consider extending their service catalogues and pushing their BYOD efforts forward, opening their corporate infrastructure for privately-owned devices while improving their security measures and usage policies. In addition, CIOs should be aware that especially young employees hold strong performance expectancies regarding BYOD outweighing the potential losses. CIOs should be aware that young employees are likely to use privately-owned devices even if it is not permitted by the enterprise. As BYOD is difficult to prevent, CIOs and IT service provider, hence, should put an emphasis on creating secure IT infrastructures in order to prevent security and privacy breaches caused by employees using their private devices at workplace. Although the research model explains a large amount of the variance in behavioral intention, we are well aware of the limitations of our approach. First, this study builds upon the UTAUT model which is intended to explain technology adoption behavior. Although the model provides a reasonable goodness fit, further research should also concentrate on other theoretical approaches which, for instance, also take an individual s relationship with technology into account. Second, our data suggests that more work needs to be done to frame the perceived threats construct more concrete. We suggest that further research could focus on theories from (service) marketing research such as the perceived risk theory (Bauer, 1967). Third, this study solely focuses on students as future employees. Although only students with relevant work experience have been selected (approx. 3 months), their work experience and experiences in dealing with corporate IT devices is still limited. Fourth, the data sample does only cover German students. It would be interesting to examine if there are any cross-cultural differences regarding the determinants of BYOD adoption behavior. Hence, further research should take a broader, crosscultural perspective. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,

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12 APPENDIX A Latent Variable Code Measurement (measured with a five-point Likert scale) Origin Performance Expectancy Effort Expectancy Social Influence Perceived Business Threats Perceived Private Threats BYOD Influenced Employer Attractiveness Behavioral Intention PE1 I would find the service useful in my job. TAM PE2 Using the service would increase my effectiveness on the job. TAM PE3 Using the service would increase my job motivation. UTAUT PE4 Using the service would increase my productivity on the job IDT EE1 Using the service would take too much time from my normal duties. MPCU EE2 Learning to use the service would be rather difficult for me. TAM EE3 It would take too long to learn how to use the service to make it worth the effort. MPCU I predict that, SI1 people who are important to me think that I should use the services. TAM SI2 people in a company who use the services have more prestige than those who do not. IDT SI3 people in a company who use the services have a high profile. IDT Using a "Bring Your Own Device" service increase the risk that PT-B1 I lose business data self-designed PT-B2 I violate company policies self-designed PT-B3 I corrupt the corporate network with malware self-designed PT-B4 I am not able to work due to a service failure self-designed Using a "Bring Your Own Device" service increase the risk that PT-P1 I lose private data self-designed PT-B2 too restrictive corporate policies limit the usage of my private device self designed PT-P3 private data can be viewed by my company self-designed PT-P4 increasingly blurred boundary between work and private life endanger my private life self-designed EA1 EA2 I prefer employers during the search for employment which provide a "Bring Your Own Device" service instead of other employers which do not provide such a service. The employer attractiveness of a company would be increased by a provided "Bring Your Own Device" service. self-designed self-designed BI1 If a "Bring your Own Device" service is offered, I intend to use the service. UTAUT BI2 If a "Bring your Own Device" service is offered, I predict I would use the service. UTAUT BI3 If a "Bring your Own Device" service is offered, I plan to use the service. UTAUT TAM: Technology Acceptance Model, Davis (1989) IDT: Innovation Diffusion Theory, Moore and Benbasat (1991) UTAUT: Unified Theory of Acceptance and Use of Technology, Venkatesh et al. (2003) MPCU: Model of PC Utilization, Thompson et al. (1991) Table A1: Measurement Items and Descriptive Statistical Analysis Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17,