2013 amcis byod postreview final

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BYOD - The Next Big Thing in Recruiting?Determinants Of IT-Consumerization AdoptionFrom the Perspective of Future Employees

CONFERENCE PAPER · AUGUST 2013

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Retrieved on: 11 September 2015

http://www.researchgate.net/profile/Andy_Weeger2?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_4http://www.researchgate.net/institution/Hochschule_Neu-Ulm?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_6http://www.researchgate.net/institution/Hochschule_Neu-Ulm?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_6http://www.researchgate.net/?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_1http://www.researchgate.net/profile/Heiko_Gewald?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_7http://www.researchgate.net/institution/Hochschule_Neu-Ulm?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_6http://www.researchgate.net/profile/Heiko_Gewald?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_5http://www.researchgate.net/profile/Heiko_Gewald?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_4http://www.researchgate.net/profile/Andy_Weeger2?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_7http://www.researchgate.net/institution/Hochschule_Neu-Ulm?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_6http://www.researchgate.net/profile/Andy_Weeger2?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_5http://www.researchgate.net/profile/Andy_Weeger2?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_4http://www.researchgate.net/?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_1http://www.researchgate.net/publication/257757704_BYOD_-_The_Next_Big_Thing_in_Recruiting_Determinants_Of_IT-Consumerization_Adoption_From_the_Perspective_of_Future_Employees?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_3http://www.researchgate.net/publication/257757704_BYOD_-_The_Next_Big_Thing_in_Recruiting_Determinants_Of_IT-Consumerization_Adoption_From_the_Perspective_of_Future_Employees?enrichId=rgreq-9ab9ff89-8047-405c-bbf1-d7a5feffaea9&enrichSource=Y292ZXJQYWdlOzI1Nzc1NzcwNDtBUzoxODM0MzAzMjMxMjIxNzZAMTQyMDc0NDYwNTYyMg%3D%3D&el=1_x_2


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Loose et al. Examining the Determinants of BYOD Service Adoption Behavior

Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17, 2013. 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 ManagementNeu-Ulm University

Wileystr. 1, 89231 Neu-UlmGermany

[email protected]

Andy Weeger

Center for Research on Service ScienceNeu-Ulm University


[email protected]

Heiko Gewald

Center for Research on Service ScienceNeu-Ulm University


[email protected]

ABSTRACT

Bring Your Own Device (BYOD) enables employees to use their privately-owned devices for business purposes. There is anongoing debate on the costs, benefits and potential threats of this concept amongst practitioners. Surprisingly, employees andtheir expectations and attitudes towards BYOD are rarely part of these discussions. Contributing to this research area, thisstudy 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 inGermany.

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, thesignificant impact of behavioral intention to use a BYOD service on employer attractiveness indicates that the offering ofBYOD 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


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INTRODUCTION

IT departments have invested a lot of effort in building up standardized service offerings for their clients. These efforts havefrequently 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, businessmobility is becoming an essential part of business operations (Harris et al., 2012). In this regard, standardized servicesoffered by the IT department do frequently not cover the requirements imposed on the employees. Additionally, employeeswant 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 businesscapabilities and the consumerization of IT (Harris et al., 2012). If these trends could not be sufficiently addressed bycorporate IT, firms may find themselves overwhelmed with innovations aimed at the consumer sector spilling over in thecorporate 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 acorporate environment as they use in their private lives (Holtsnider et al., 2012). Throughout this paper, we apply thedefinition of Niehaves et al. (2012, p. 2) who regard IT consumerization as a scenario in which employees "invest their ownresources to buy, learn, and use consumer technology at their workplace". Using consumer devices for business purpose iscurrently the most visible kind of IT consumerization behavior. Therefore, the current discussion of IT consumerizationprimarily 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 toas "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 theircustomers (Györy et al., 2012). Offering a BYOD service implies that firms open their networks and enable data-access toconsumer devices. Furthermore, adopting a BYOD service means that employees have to accept policies regarding the usageof their privately-owned devices for business purposes. These policies are established in order to safeguard security and togovern liability (Vogel et al., 2010; Harris, 2012). Subsequently, we define BYOD service as a service offered by corporateIT that allows employees to bring privately owned device to the workplace, to connect them to the corporate network and touse them for business purposes.

This type of service offering is subject to an extensive debate among IT executives. These debates often address the questionwhether 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 insecurity technology (Harris et al., 2012), as well as management and operation concepts (Weiß et al., 2012). On the otherhand, anecdotal evidence indicates that there is a high level of motivation to use private devices in business contexts. As theyhave been born into the ‘digital age’ in which technology is ubiquitous (Prensky, 2001), especially young employees arewilling and sometimes even demanding to use their own devices for business related tasks. Furthermore, the use of privately-owned devices is difficult to prevent. Therefore, offering a BYOD service could be a reasonable step to prevent the rise ofuncontrollable 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 researchdoes 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 asthe drivers of this trend. Therefore, the focus of this study has been on students in their final years of study, i.e. the futureemployees. In order to gain understanding on the factors driving young employees to adopt BYOD services, we postulate thefollowing 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), inparticular SMEs are looking for ways to remain and become attractive for young employees. Assuming that future employeesare 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 thesefindings, the development of the research model and the subsequent research methodology is presented. Finally, the resultsare discussed, conclusions are drawn and suggestions for future research are presented.


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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 findingis supported by the structured literature review provided by Niehaves et al. (2012) who identified only 22 scientific papersfocusing on consumerization of IT / BYOD. However, most scholars discuss IT consumerization as a result of a reversedtechnology adoption life cycle, initiated by employees demanding the same ease of use with corporate IT as consumerproducts offer (Cummings et al., 2009). IT consumerization is usually defined as the dual use of devices, applications, andservices for private and business purpose (Ingalsbe et al., 2011; Holtsnider et al., 2012). Harris et al. (2011, p. 2), forinstance, define consumerization of IT as “the adoption of consumer application, tools, and devices in the workplace.” Otherauthors, such as Niehaves et al. (2012), regard the ownership as key characteristic and conceptualize IT consumerization as aphenomenon 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 associatedwith greater autonomy for employees (Niehaves et al., 2012). Additionally, employees perceive that technology which theychose 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 severaldisadvantages for employees and organizations (Niehaves et al., 2012). Security issues, increasing complexity, loss of controland performance issues are potentially negative effects for organizations. In this regard, Weiß et al. (2012) discuss theimpacts on corporate IT at different stages such as information management, information exchange, information systems andinformation technology. They conclude that the challenges for IT management in organizations increase without a predictableend. 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 realizingproductivity 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 discussedkind of IT consumerization behavior. This sub-trend of IT consumerization is commonly referred to as BYOD. We defineBYOD as the act of bringing personally owned device to the workplace, connecting them to the corporate network and usingthem for business purposes. The BYOD concept can be extended into a complete set of BYOD services. A BYOD service isdefined 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 BYODservices 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 shoppingcart which is financed by the company and may also be used for private purposes (Lang, 2012); whereas “We Sponsor YourDevice” (WSYD) implies that the employee receives a financial compensation for using a privately-owned device forbusiness purpose (Vogel et al., 2010). For this study we adopted the following conception of a BYOD service: A BYODservice 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 thesetheories are based on the premise that human beings are rational and that they consider the implications of their actionsbefore they decide whether to perform a certain action or not. These theories conceptualize intention as a function of beliefsabout the likelihood that performing a particular behavior will lead to a specific outcome. The stronger the intention toengage 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 anindividual's intention to use a technology. These determinants account for positive outcomes associated with using atechnology. In the same vein, other technology acceptance models such as the Unified Theory of Acceptance and Use ofTechnology (UTAUT) (Venkatesh et al., 2003) hypothesized that the advantages of using a technology and conditionsfacilitating 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 toexplain individual’s service adoption have been reviewed. These studies indicate that technology acceptance models arecapable to explain an individual’s adoption behavior in regard to technology based services (Featherman et al., 2003; Amberg


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et al., 2004; Pagani, 2004; Hung et al., 2005; Carlsson et al., 2006; Chen et al., 2007; Mallat et al., 2008). Almost all of thecited studies show that perceived usefulness and ease of use are important determinants of an individual’s service adoptionbehavior. 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, Carlssonet al. (2006) found no significant impact of social influence on intention. In contrast, the data of Mallat et al. (2008) indicatesan impact of social influence on intention. Overall, these studies confirm the capability of technology acceptance models toexplain the determinants of service adoption. Nevertheless, Carlsson et al. (2006) as well as Featherman et al. (2003) pointout 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 technologyacceptance models (Venkatesh et al., 2003), it seems to be suitable to contribute explanations on the factors influencingBYOD service adoption by future employees. Although, the UTAUT –such as other technology acceptance models– solelyfocuses 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) wasincluded. Furthermore, facilitating conditions which encompasses organizational and technical infrastructure facilitatingtechnology 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 studyfocuses on students in their last semesters with relevant work experience (gained in internships and/or practical semesters andsince the majority of these students are not employed in a company, they are not able to assess conditions facilitating the dualuse of devices for business and private purposes. Finally, employer attractiveness (EA) was included in order to address thesecond research question. Table 1 provides the definition of all constructs within the model.

Table 1: Construct definitions

Construct Definition TypeNumber

of items

Performance Expectancy (PE)

The degree to which an individual believes thatusing the BYOD service will help him or her toattain gains in job performance (Venkatesh et al.,2003).

Reflective 4

Effort Expectancy (EE) The degree of ease associated with the use of theservice (Venkatesh et al., 2003).

Reflective 3

Social Influence (SI)The degree to which an individual perceives thatimportant others believe he or she should use theservice (Venkatesh et al., 2003).

Reflective 3

Perceived Threats (PT)

The degree to which an individual believes that theuse of a BYOD service is associated with threatsthat are evoking anxious or emotional reactions(Joshi, 1991; Compeau et al., 1999).

Formative 8

Perceived Business Threats (PT-B)The degree to which an individual believes that theusage of a BYOD service is threatening his or her

job.Formative 4

Perceived Private Threats (PT-P)

The degree to which an individual believes that the

usage of a BOYD service is threatening his or herprivate life.

Formative 4

Employer Attractiveness (EA)The degree to which the attractiveness of a firm forincreases if the firm offers a BYOD service.

Reflective 2

The constructs and their proposed relationships are depicted in Figure 1. Assuming that the UTAUT model is already wellknown in the IS community, only the modifications of the UTAUT model are discussed in detail below.


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Figure 1: Research Model

Perceived Threats

Using an innovation must not always be perceived as beneficial. As the UTAUT model does not cover negative factorsinhibiting BYOD service adoption, perceived threats (PT) was added. This construct was derived from Social CognitiveTheory (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 thedegree 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. Inaccordance to Niehaves et al. (2012), these factors can be distinguished between two dimensions: threats addressing theprivate life and threats addressing business life. Regarding private life, a loss of private data, the retrieval of private data bythe 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 corporatenetwork 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 anindividual’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 foractual and future employees is of particular interest. If BYOD service offerings impact an individual’s decision-making abouttheir actual or future employers, BYOD services could be used as an argument for both recruiting new employees andretaining actual employees. To examine if a BYOD service offering contributes to the attractiveness of an employer, BYODinfluenced 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 aBYOD service as an attractive employer. Hence, it is hypothesized that behavioral intention to adopt a BYOD servicepositively 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 onfuture employees' intention to use BYOD services. To minimize bias due to different backgrounds, the focal group consistsonly of students with business and engineering specialization studying in a small German university. Facing a shortage ofyoung engineers and specialized managers (Köchling, 2003), these students are particularly attractive to medium-sized andlarge industrial enterprises. These companies, in turn, are heavily keen on understanding BYOD service adoption to guidetheir decisions whether to offer a BYOD service or not. In order to ensure that students can realistically assess BYOD, onlystudents with relevant work experience (due to an internship or practical semester) have been asked to participate.


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To retain measurement validity, items were –as far as possible– taken from prior technology adoption research. In order torelate the items to the BYOD context, the wording of the items was slightly altered. Prior literature constituted the basis fordeveloping 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 theconstructs. Regarding the formative nature of PT, additional tests have been conducted to ensure that all indicators are clearlydistinguished and that the construct is captured in all its facets. Table 1 presents the latent variables, their types (reflective orformative), 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. Thequestionnaire was published by using an online survey tool. A pretest within the target group has been conducted to ensurethat the questionnaire understandable and unambiguous.

At total, 177 responses have been collected (response rate of approx. 20%). The responses were reviewed to ensure that eachrespondent completely finalized the survey. As a result of this analysis, we identified 93 responses with at least one missingvalue (including demographics). After eliminating the missing values, 84 usable responses left, out of which 8 have beenprovided from visiting international students. The key demographics are depicted in Table 2.

Table 2: Demographics (N=84)

Respondents Percentage

GenderMale 57 68%

Female 27 32%

Age

< 18 years 0 0%

18 - 21 years 15 18%

22 - 25 years 45 54%

26 - 29 years 21 25%

> 29 years 3 4%

Study

Focus

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 thelatent variables and their proposed causal relationships. There were several reasons for using partial leas squarest (PLS). PLSis a well-established algorithm for technology acceptance research (Venkatesh et al., 2003), PLS makes fewer demands onthe 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. Firstthe outer model (component measurements) and subsequently the inner model (structural causal paths) has been evaluated.


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Table 3: Cross loadings

BI EE PE EA SI

BI1 0.926 -0.338 0.671 0.606 0.326BI2 0.919 -0.392 0.605 0.526 0.328

BI3 0.914 -0.344 0.618 0.498 0.342

EE1 -0.309 0.790 -0.323 -0.278 -0.022

EE2 -0.365 0.853 -0.269 -0.175 0.015

EE3 -0.280 0.834 -0.304 -0.239 0.272

PE1 0.610 -0.328 0.865 0.608 0.127

PE2 0.619 -0.360 0.891 0.467 0.154

PE3 0.612 -0.314 0.812 0.424 0.284

PE4 0.457 -0.185 0.814 0.443 0.287

PEA1 0.556 -0.153 0.539 0.899 0.203PEA2 0.490 -0.345 0.475 0.868 0.191

SI1 0.387 -0.023 0.237 0.273 0.867

SI2 0.115 0.222 0.132 0.071 0.697

SI3 0.208 0.182 0.161 0.066 0.752

To analyze the component measurements, Average Variance Extracted (AVE), Composite Reliability (CR), Cronbach’sAlpha (CA), and Cross loadings were calculated. The cross loadings provided in Table 3 were assessed according to theguidelines of Gefen et al. (2005). All measurement items loaded greater than .707 onto their respective latent variables andwere 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 orthree items, we followed the advice of Chin (1998) and chose CR as an additional measure. All constructs exceed thethreshold 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 indicatorsassigned to it than with any other LV. As depicted in Table 5, the square root of the AVE is higher than the LV's correlationwith any other LV. Therefore, discriminant validity can be assumed.

Table 4: CR, CA and AVE values of the reflective constructs

Composite Reliability

(CR)

Cronbach’s Alpha

(CA)

Average Variance

Extracted (AVE)

BI 0.943 0.909 0.845

EA 0.877 0.720 0.781

EE 0.866 0.769 0.683

PE 0.910 0.868 0.716

SI 0.818 0.733 0.601

In contrast to the other latent variables, PT was measured using formative items. Furthermore, the construct includes twometa-facets: the private dimension (PT-P) and the business dimension (PT-B). Therefore PT was modeled as a second orderconstruct, whereas PT-P and PT-B capture both facets of PT. The latent variables PT-P and PT-B are measured by formativeindicators. PT itself is captured by the formative indicators associated to the lower order components PT-P and PT-B. Thismethod 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


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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).

Table 5: Latent variable correlation and Fornell-Larcker criterion

PT-B BI EA EE PT-P PE PT SI

B-PT 0.000

BI -0.416 0.919

EA -0.352 0.593 0.884

EE 0.185 -0.389 -0.275 0.826

P-PT 0.555 -0.416 -0.253 0.176 0.000

PE -0.396 0.688 0.576 -0.359 -0.309 0.846

PT 0.878 -0.494 -0.355 0.230 0.877 -0.421 0.000

SI 0.056 0.361 0.223 0.095 -0.045 0.245 -0.014 0.775

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 eachendogenous LV's coefficient of determination (R²) have calculated1. Results of the structural model estimation are illustratedin Figure 2Fehler! Verweisquelle konnte nicht gefunden werden.. The significance level is above 0.01 for all pathcoefficients. In total, the model accounted for a significant amount of variance in BI (R2 = 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 aBYOD service is correlated with the attractiveness of companies, a modified UTAUT model was proposed. In contrast toprior UTAUT studies, this study also takes into account potential threats associated with the usage of privately-own devicesfor 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.


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Figure 2 provide support for almost all hypotheses, this study shows that the proposed research model is capable to explainBYOD service adoption behavior. In addition, a highly significant correlation between the attractiveness of a company forfuture 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 priorUTAUT studies. In particular the strong effect of performance expectancies on behavioral intention confirms previousresults. Overall, performance expectancies have the strongest influence on behavioral intention (f 2 = .337, medium effect).This finding suggests that expectancies regarding gains in job performance due to increased mobility and flexibility are themost important factors leading future employees to adopt BYOD services. Therefore, it could be concluded, that youngemployees regard the devices they choose and use in private contexts as superior to those provided by corporate IT. On theother hand, future employees also seem to take the sacrifices of BYOD into account. Both, effort expectancies (f 2 = 0.081,weak effect) and perceived threats (f 2 = 0.137, weak effect) were found to significantly contribute to their behaviouralintention. First, the findings show that future employees are aware of the efforts related with using their devices for businesspurposes (e.g. setting the devices up for work). Second, the results regarding perceived threats indicate that future employeesin Germany are aware of the risks associated with BYOD. Furthermore, private and business risk perceptions are pretty muchbalanced. 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 ofperformance expectancies. This finding indicates that the performance effects expected by future employees outweigh theefforts and the potential threats. These expectations are capable to give an explanation why especially young employees arewilling 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 (f 2 = 0.162, weakeffect). 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 ispositively correlated with his or her intention to adopt a BYOD service. Furthermore, descriptive data reveals that Germanbusiness and engineering students have a strong tendency to use privately-owned devices for business purposes. Taking thesefindings into consideration, it can be argued that BYOD services can indeed play a vital role in recruiting these students asfuture employees. Regarding the strong performance expectancies of young employees associated to BYOD, the dual use ofconsumer 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 findingcontrasts the data captured in a recent study among 600 senior business and IT leaders in 17 countries. This study found thatonly 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 moreappealing to younger employees. As consumerization of IT will gain momentum in future (Fenn et al., 2011), the findings ofour 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 pushingtheir BYOD efforts forward, opening their corporate infrastructure for privately-owned devices while improving theirsecurity measures and usage policies. In addition, CIOs should be aware that especially young employees hold strongperformance expectancies regarding BYOD outweighing the potential losses. CIOs should be aware that young employeesare likely to use privately-owned devices even if it is not permitted by the enterprise. As BYOD is difficult to prevent, CIOsand 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 thelimitations of our approach. First, this study builds upon the UTAUT model which is intended to explain technology adoptionbehavior. Although the model provides a reasonable goodness fit, further research should also concentrate on othertheoretical approaches which, for instance, also take an individual’s relationship with technology into account. Second, ourdata suggests that more work needs to be done to frame the perceived threats construct more concrete. We suggest thatfurther 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 havebeen 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-culturaldifferences regarding the determinants of BYOD adoption behavior. Hence, further research should take a broader, cross-cultural perspective.


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REFERENCES

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Ajzen, I. (1991) The theory of planned behavior, Organizational Behavior and Human Decision Processes, 50, 2, 179-211.2. Amberg, M., Hirschmeier, M., and Wehrmann, J. (2004) The Compass Acceptance Model for the analysis and

evaluation of mobile services, Int. J. Mob. Commun., 2, 3, 248-259.3. Avanade (2012) Global Survey: Dispelling Six Myths of Consumerization of IT, Avanade, Seatle.4. Bauer, R. A. (1967) Consumer Behavior as Risk Taking in Risk Taking and Information Handling in Consumer

Behavior, D. F. Cox (ed.), Harvard University Press, Cambridge, USA, 23-33.5. Bocker, J., and Klein, M. (2012) ICT-Innovationen erfolgreich nutzen: Wie Sie Wettbewerbsvorteile für Ihr

Unternehmen sichern, Gabler.6. Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J., and Walden, P. (2006) Adoption of Mobile Devices/Services -

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10. Chin, W. W., Marcolin, B. L., and Newsted, P. R. (2003) A Partial Least Squares Latent Variable Modeling Approachfor Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and Voice Mail Emotion/AdoptionStudy, Information Systems Research, 14, 2, 189-217.

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12. Compeau, D. R., and Higgins, C. A. (1995) Application of Social Cognitive Theory to Training for Computer Skills, Information Systems Research, 6, 2, 118-143.

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Bloomington, Indiana, USA, pp. 257-264.14. Davis, F. D. (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS

Quarterly, 13, 3.15. Featherman, M. (2001) Extending the technology acceptance model by inclusion of perceived risk, AMCIS Proceedings.16. Featherman, M. S., and Fuller, M. (2003) Applying TAM to E-Service Adoption: The Moderating Role of Perceived

Risk, HICSS Proceedings, Hawaii.17. Fenn, J., and LeHong, H. (2011) Hype Cycle for Emerging Technologies, Gartner.18. Fornell, C., and Larcker, D. F. (1981) Evaluating Structural Equation Models with Unobservable Variables and

Measurement Error, Journal of Marketing Research, 18, 39-50.19. Gefen, D., and Straub, D. (2005) A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated

Example, Communications of the Association for Information Systems, 16, Article 5.20. Györy, A., Cleven, A., Falk, U., and Brenner, W. (2012) Exploring the Shadows: IT Governance Approaches to User-

Driven Innovation, ECIS Proceedings.

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Harris, C. (2012) Mobile Consumerization Trends & Perceptions - IT Executive and CEO Survey, Decisive Analytics,LLC.22. Harris, J. G., and Junglas, I. (2011) The Genie Is Out of the Bottle: Managing the Infiltration of Consumer IT Into the

Workforce, Accenture Institute for High Performance.23. Harris, M., Patten, K., Regan, E., and Fjermestad, J. (2012) Mobile and Connected Device Security Considerations: A

Dilemma for Small and Medium Enterprise Business Mobility, AMCIS Proceedings.24. Holtsnider, B., and Jaffe, B. D. (2012) IT Manager's Handbook: Getting Your New Job Done, Morgan Kaufmann.25. Hulland, J. (1999) Use of Partial Least Squares (PLS) in Strategic Management Research: A Review of Four Recent

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Consumerized Enterprise – an overview of considerations, AMCIS Proceedings.


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APPENDIX A

Latent Variable Code Measurement (measured with a five-point Likert scale) Origin

Performance

Expectancy

PE1 I would find the service useful in my job. TAMPE2 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

Effort Expectancy

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

Social Influence

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

Perceived

Business

Threats

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

Perceived

Private

Threats

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

BYODInfluenced

Employer

Attractiveness

EA1 I prefer employers during the search for employment which provide a "Bring Your OwnDevice" service instead of other employers which do not provide such a service.

self-designed

EA2 The employer attractiveness of a company would be increased by a provided "Bring Your

Own Device" service.

self-designed

Behavioral

Intention

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

2013 amcis byod postreview final

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