Job performance through mobile enterprise systems: The role of organizational agility, location independence, and task characteristics

Information & Management 51 (2014) 605–617

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Information & Management journal homepage: www.elsevier.com/locate/im

Job performance through mobile enterprise systems: The role of organizational agility, location independence, and task characteristics§ Sunghun Chung a,*, Kyung Young Lee b,1, Kimin Kim b,1 a b

McGill University, Canada Bishop’s University, Canada

A R T I C L E I N F O

A B S T R A C T

Article history: Received 2 October 2013 Received in revised form 7 January 2014 Accepted 12 May 2014 Available online 23 May 2014

This study examines how organizational workers improve their perceived job performance through the use of Mobile Enterprise Systems (MES), while also investigating the impact of perceived organizational agility and location independence on technology acceptance of MES. This study also tests the moderating role of task characteristics (task significance and feedback) on the relationship between MES usage and perceived job performance. Based on the extant technology acceptance model (TAM), we proposed an extended TAM and conducted a large-scale survey among organizational workers who use MES in their workplace across industries. Our findings suggest that both positive attitude toward MES and a high level of habitual MES usage are positively associated with perceived job performance, and that task characteristics positively moderate the relationship between habitual usage (attitude toward MES) and perceived job performance. More importantly, we also found that organizational agility is positively associated with both perceived ease of use and perceived usefulness, while location independence is positively associated with perceived ease of use. The present findings provide us with a deeper understanding of how organizational workers utilize MES and how they improve their perceived job performance through the use of MES. Based on these findings, we discuss further implications and limitations. ß 2014 Published by Elsevier B.V.

Keywords: Mobile enterprise systems Perceived job performance Organizational agility Location independence Task characteristics

1. Introduction The explosive growth of mobile technologies over the last few years has created over one billion smartphone owners, many of whom are professionals using their device during work [5]. The ubiquitous accessibility of information through mobile devices has led to an increased mobility of workers from their fixed workplaces. Market researchers estimate that by 2016, 350 million workers will be using their smartphones for business purposes, and the use of smartphones will offer new business benefits [24]. For these reasons, various organizational aspects concerning

§ The authors alone are responsible for all limitations and errors that may relate to the study and the paper. * Corresponding author at: Desautels Faculty of Management, McGill University, 1001 Sherbrooke Street West, Montreal, QC H3A 1G5, Canada. Tel.: +1 514 398 2768; fax: +1 514 398 3876. E-mail address: [email protected] (S. Chung). 1 Williams School of Business, Bishop’s University, Lenoxville, QC J1M 1Z7, Canada.

http://dx.doi.org/10.1016/j.im.2014.05.007 0378-7206/ß 2014 Published by Elsevier B.V.

‘‘mobile work’’ have received a great deal of recent attention. Moreover, many CIOs plan to allocate a considerable amount of their budgets in mobile enterprise systems (MES) [42]. Enterprises are now adopting mobile technologies for mobile enterprise systems (MES), which are designed for employees of a specific company to access their internal IT systems, so as to increase their efficiency and improve their competitiveness [58]. While business productivity and agility have long been recognized as important IT management concerns [40], recent research has pointed out the necessity to shift attention to the impact of mobile computing on employees’ workplace performance [62,68]. Most importantly, MES have played a significant role in the explosive growth of mobile computing in the workplace [1,2]. MES can be defined as business enterprise systems, including critical business functions that enable users to access enterprise systems via wireless mobile devices, such as smartphones or tablets [9,55]. Specifically, MES enable users to access the Intranet and internal email, manage projects and documents, provide customer relationship management (CRM), and conduct enterprise resource planning, with simple features and functionalities that help users

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complete specific tasks via their mobile devices. Thus, by using MES, organizational workers can remotely access and update enterprise databases from any location and at any time, and can even improve firm sales [2,47]. These characteristics of MES can stimulate users’ efficiency and effectiveness with regard to taskrelated issues under various circumstances [1]. Further, MES foster collaboration among users across various functional units in a firm, and also facilitate collaboration with other firms and business partners of interest [58]. While the impact of MES on organizational workers’ productivity has been given considerable attention in various literatures [12,20], relatively little research effort has been made to examine how MES actually lead to users’ job performance. In particular, we have a limited understanding of what the key antecedents are of such an MES usage outcome. In this paper, we focus on users’ perceived job performance as the outcome of MES use, which indicates the successful role of MES with regard to employees’ tasks. The objectives of this study are to address the following research questions: (1) How can users successfully manage MES in order to enhance their perceived job performance gain from MES? (2) What internal and/or external factors are important antecedents in increasing MES users’ acceptance of MES and their perceived job performance gain from MES? (3) Which task-related circumstances amplify users’ perceived job performance gain from MES? To answer these questions and provide a better understanding of the relationships among MES, organizational environment, and perceived job performance gain from MES, we develop a theoretical model that extends the technology acceptance model [TAM, see 11, 60, 61] by including specific variables, such as the task characteristics of MES users, habitual MES use, users’ location independence, and the characteristics of organizations (i.e., organizational agility). Specifically, we examine the relationships among habitual MES use [35,44], attitude toward MES [3,66], and perceived job performance [16,39,43], while considering the moderating effects of task feedback and task significance [18,25]. Of special importance, we examine the key antecedents of core constructs regarding beliefs about MES (i.e., perceived ease of use and usefulness), such as perceived organizational agility as a user’s perceived external factor [37,52] and a user’s location independence as an internal factor [41]. From a large-scale survey among organizational workers who use MES in their workplace across multiple industries, we found that both a higher positive attitude toward MES and a high level of habitual MES usage lead to greater perceived job performance. Interestingly, we found a positive moderating effect of both task feedback and task significance on the relationship between perceived job performance and its antecedents. We identified individual users’ perceived organizational agility and location independence as the key antecedents that are positively associated with both perceived ease of use and usefulness of MES.

2. Theoretical background 2.1. Mobile enterprise systems (MES) as an agility enhancing system Since there have been vast improvements in enterprise systems during the last decade, MES have created an any time/anywhere workplace that has changed the traditional office work environment by stimulating flexibility [20]. Also, due to the nature of strong mobility, MES facilitate the internal operational aspects of business, e-transactions and large-scales information broadcasts to mobile gadgets, providing users with various sorts of information, such as schedules and meeting agendas [12,58].

From a survey among approximately 200 CIOs, a recent study identified the situations in which competitive advantages of MES could be realized [55]. According to the study, MES enable specific information to be ubiquitously available. Thus, MES users can access and receive ad-hoc information (e.g., a user of Mobile CRM (a salesperson) can obtain information about a specific customer’s location). Moreover, MES improve business processes by enabling users to participate ubiquitously in workflow processes and by capturing higher transparency regarding current workflow states. Finally, MES unify communication channels so that users can reduce efforts to obtain information, thereby experiencing less work disruption caused by information defects. In general, along with benefits at the individual level, MES benefits can be observed at the enterprise level. By adopting MES, firms can lower information management costs, reduce processing time, provide job processes ubiquitously, and encourage mobile collaboration among their employees. These benefits from implementing MES actually improve organizational agility [52], since prompt information availability, shorter projected time and any time/anywhere work collaboration should help organizations cope with market and demand changes quickly, efficiently, and effectively. For these reasons, in this paper we consider MES as a type of agility enhancing enterprise system that improves the competitive advantage of a firm. In addition, we admit that MES adoption is crucial for organizations to enhance their organizational performance. 2.2. Extended technology acceptance model (TAM) for MES The technology acceptance model (TAM) [11] posits that people’s beliefs about a technology, namely its perceived ease of use (PEU) and perceived usefulness (PU), positively influence their attitude toward using the technology (ATU) and behavioral intentions to use it (BI), which in turn, influence actual use. Since mobile data communication devices and services were introduced in the early 2000s, many IS scholars have attempted to develop, extend, and empirically validate TAM for various mobile systems. For this study, in order to determine the gap in the current literature on TAM for mobile systems and identify the antecedents and consequences of TAM that are relevant to MES usage, we reviewed 22 empirical studies published in peer-reviewed journals during the last 10 years (since 2003). Table 1 summarizes (1) the antecedents of two key beliefs about mobile systems (PEU and PU); (2) other factors that are found to influence user acceptance of mobile systems; (3) the consequences of mobile systems use; and (4) the moderating impacts identified in TAM for mobile systems. The literature review provided us with a number of interesting insights. Based on these insights, we suggest an extended TAM for MES by proposing supplemental variables, such as individuals’ internal and external antecedents regarding beliefs about MES, moderating factors, and a consequence (individual performance) of one’s attitude toward and usage of MES. 2.2.1. Extending TAM for MES with perceived performance as a consequence of system use To the best of our knowledge, little effort has been made so far to further extend ‘‘TAM for mobile services’’ to identify and test the consequence of system usage. A majority of the articles in our literature review contained only behavioral intentions to use and did not extend their research models further to actual usage; in addition, none of the empirical papers in our literature review (on TAM for mobile contexts) investigated the performance impact of system usage. We argue that the main reason for not having performance measures in the extant studies is that the majority of focal mobile services in these studies were consumer-oriented services (e.g., mobile Internet or mobile commerce services) rather

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Table 1 Literature review on technology acceptance models in mobile context. Studies

Antecedents of PEU

Antecedents of PU

Other antecedents of attitude or usage (intentions)

Cyr, Head [10]

Design Esthetics

Design Esthetics

Enjoyment

Fang, Chan [13]

Hong, Thong [22]

Perceived playfulness Perceived Security Confirmation

Huang, Lin [23]

Confirmation

Satisfaction

Perceived Mobility Value

Perceived Enjoyment

Kim, Chan [28]

Kim and Garrison [30] Kim and Han [27]

Lee, Cheung [33]

Liu, Tian [36]

Lo´pez-Nicola´s, Molina-Castillo [31]

Luarn and Lin [38]

Task-carried context Availability of

Task-carried context Availability of resources Subjective norms

resources Subjective norms Social Influence Social Influence Perceived flexibility benefits Perceived status benefits Attitude toward mobile innovations Perceived Self-efficacy

Mallat, Rossi [41]

Park and Chen [46] Self-Efficacy Qi, Li [48]

Schierz, Schilke [53]

Thong, Hong [56]

Perceived enjoyment Confirmation

Wang, Lo [63]

Perceived compatibility Individual mobility Confirmation

Wu, Wang [65]

Yang [67]

Compatibility Self-efficacy Technical support and training Innovativeness Past adoption behavior Knowledge Technology cluster

Compatibility Self-efficacy Technical support and training Innovativeness Past adoption behavior Knowledge Technology cluster

Mobile-loyalty

M-commerce Loyalty Wireless hand-held devices

Usage intentions Task Type (PU, PEU ! Usage Intentions) Continued usage

intentions Usage intentions

Mobile Internet

Mobile Learning

Usage intentions ! Actual usage

Smartphones

Usage intentions

Mobile Internet

Hedonic Value Social Value

Adoption intentions

Mobile Data service

Perceived Enjoyment

Usage intentions

Subjective norms

Symbolic adoption ! TTF ! Actual usage

Multimedia Messaging Service Mobile Office Service

Attitude toward mobile innovations

Usage intentions

Mobile 3G technologies

Perceived Self-efficacy Perceived Credibility Perceived Financial Cost () Compatibility Mobility ! Usage context Self-Efficacy

Usage intentions

Mobile banking

Usage intentions

Mobile ticketing

Voice service satisfaction Brand experience Innovation experience Flow experience Perceived compatibility Perceived security Subjective norm

Technology specific valuation Number of users

Wu and Wang [64]

Research context

Mobile Internet

Perceived ubiquity Perceived reachability External Influence Interpersonal Influence Perceived Media Perceived Media Richness Richness

Usage constructs

Perceived Value ! Adoption Intentions

Enjoyment Technicality () Perceived Fee () Perceived cost savings Firm’s willingness to fund

Kim [29]

Moderators

Perceived Risk () Cost () Compatibility Compatibility

Job Relevance (PU ! Usage Intentions Job Relevance (PU ! Usage Intentions

Attitude toward usage

Smartphone adoption Attitude toward usage ! Mobile Internet Usage intentions

Attitude toward usage ! Usage intentions Satisfaction ! Continued usage intentions Usage intentions ! Actual usage

Mobile payment service

Usage intentions ! Actual usage Usage intentions

Mobile commerce services

Attitude toward usage

Mobile Internet

Multimedia Messaging Services

Mobile healthcare services

Mobile commerce services

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608 Table 1 (Continued ) Studies

Yuan, Archer [69]

Antecedents of PEU

Antecedents of PU

Other antecedents of attitude or usage (intentions)

Fit between Task (Mobility, Location dependency, Time criticality) and Technology (Mobile work support)

than organizational worker-oriented services (e.g., mobile enterprise systems). Therefore, it may not have been possible for researchers to identify or measure the performance differentials stemming from the use of mobile services. However, MES are ‘‘enterprise’’ mobile services rather than consumer-oriented ones that are intended to improve users’ work processes and performance. With this line of thinking, we extend TAM for MES by including the impact of usage, i.e., job performance of organizational workers, in our research model. Among various performance measures at the individual level, we focus on perceived job performance gain from information systems [4,16,26], and this measure reflects organizational workers’ overall cognitive gain from using MES for their job performance. 2.2.2. Extending TAM for MES with a new set of antecedents for PU and PEU As shown in Table 1, various antecedents of PEU, PU, attitude, and usage (intentions) are found in the extant studies on mobile systems. Among these antecedents, we found that most studies frequently investigated the extent to which a system user is mobile (user mobility) and the extent to which a system is ubiquitous (system ubiquity) [23,30,41,69]. Also, many studies looked into the individual’s task-carried context (or external influence) for users’ acceptance [31,36]. Among other frequently studied factors of user acceptance in the context of consumer-oriented mobile systems, such as enjoyment, value and cost, design, and self-efficacy, we include the two factors in our research model for MES (firmoriented mobile systems): the task-carried context of an individual worker as an external factor, and the mobility of an individual worker (or the ubiquity of the system) as an internal factor. More specifically, we identify location independence and perceived organizational agility as the key antecedents for the PEU and PU of MES. The term ‘‘ubiquity’’ emphasizes the extent to which a system exists everywhere [30], and the term ‘‘mobility’’ refers to the extent to which an end-user (not necessarily an organizational worker) moves around different locations while using a technology [53]. However, since we focus on organizational workers who use enterprise applications, we opt to use the term ‘‘location independence of an individual worker,’’ which represents the degree to which an individual worker needs to use (or access) the MES, regardless of where they are located [41]. This term can embrace not only the concept of the mobility of individual users, but also the ubiquity of a system (MES). That is, individual workers with highlevel location independence are often relocated out of their fixed office, and at the same time, they have ubiquitous access to enterprise systems embedded in their mobile hand-held devices. This factor will be important for the MES use, since MES are intended to help organizational workers access their respective enterprise systems (i.e., full-version enterprise systems) anytime and anywhere. As briefly mentioned in the previous section, we argue that MES are implemented to enhance organizational agility. Therefore, individual workers’ perceptions of organizational agility should be a relevant factor for positive beliefs about MES. In this study, we define perceived organizational agility as the extent to which

Moderators

Usage constructs

Research context

Usage intentions

Mobile work supports

individual workers perceive that their firm has the ability to (1) quickly address customers’ needs by continuously monitoring and quickly improving products/services; and (2) physically and rapidly cope with market or demand changes [37]. Thus, in our research model, we argue that these two factors are the most important antecedents for technology acceptance of MES users. 2.2.3. Extending TAM for MES with task characteristics as moderating factors Only a handful of papers have looked into the moderating factors for TAM in mobile systems. In our literature review, three studies investigated the moderating role of task- and job-relevance for the impact of PU or PEU on usage intentions [13,29,30]. As such, although task characteristics have also been found to be important moderating factors on the relationship between individuals’ systems use and their performance [49], little effort has been made in the context of mobile systems examine users’ task characteristics as moderating factors on the relationship between mobile systems usage and performance. Thus, in this study, we identify two salient task characteristics for MES users: task feedback and task significance [18,25]. Task feedback is defined as the degree to which carrying out the work activities required by the job results in workers obtaining direct and clear information about the effectiveness of their performance [17 p. 5]. Task significance is defined as the degree to which the job has a substantial impact on the lives or works of other people [17 p. 5]. By echoing these task characteristics in our research model, we suggest and validate their moderating roles on the relationship between actual (habitual) MES usage and job performance. 2.2.4. Habitual use as a proxy for actual system usage As shown in Table 1, we found that most studies on TAM for mobile services have investigated usage intentions, but only a few have looked at actual usage behaviors. Thus, these papers do not extend the model further into investigating the role of technology use on job performance. In this study, instead of usage intentions, we introduce habitual usage to operationalize an individual’s usage behavior of MES. Habitual usage of MES is defined as the extent to which an individual tends to use MES automatically and habitually [35]; therefore, it reflects an individual worker’s ongoing engagement with MES. Habitual MES usage is suitable in the context of mobile applications, since such applications installed in mobile hand-held devices (e.g., smartphones) are often used by an individual due to one’s habitualization [45]. Also, extant studies on the habitual use of IS argue that the measure of habitual usage can be a good proxy for the frequency and comprehensiveness of IS use [35]. Thus, in this study, we include habitual usage in TAM for MES and verify that the habitual MES usage is valid in measuring and is structurally linked with individual beliefs (PU and PEU), attitude, and perceived performance. 3. Research model and hypotheses Based on the extant research on organizational agility, technology acceptance in mobile contexts, system usage and task

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characteristics, we developed an extended TAM for MES in order to explore new antecedents of technology acceptance in the context of MES and to investigate the role of MES on the relationship between technology acceptance and perceived job performance gain from MES in the workplace. Our research model is depicted in Fig. 1. Based on the literature review, this section presents the research model and related hypotheses for our study. 3.1. The impact of organizational agility Organizational agility is defined as an organization’s ability to (1) respond to changes in its external environment; and (2) detect and seize market opportunities efficiently and effectively [32,52]. With enhanced organizational agility (often with the help of IT investment), organizations can sense opportunities for competitive actions in their target markets and can also prepare knowledge and skills for those opportunities [52]. That is, organizational agility enables organizations to capitalize on changes in their target markets by improving their products and services (i.e., market capitalizing agility) and helps organizations improve their internal business processes to cope with market or demand changes (i.e., operational adjustment agility) [37]. A number of extant studies have investigated the positive impact of organizational agility at the firm and team levels [32,52]. Although these studies provide us with valuable knowledge on the impact of organizational agility at the collective level, in this study we propose that individual organizational workers can perceive the organizational agility of the organization they work for as the pervasive environment of the firm. This perceived organizational agility at the individual level works as an individual worker’s external (environmental) factor for system acceptance, so that it can actually influence that person’s beliefs about a certain information system. If individual workers perceive that their organization has a high level of agility, so that they believe the organization has the ability to promptly address customers’ needs, cope with market and demand changes, and improve its product and services, they will tend to believe that the MES – i.e., the mobile applications that are intended to improve the agility of the firm – are useful and easy to use for the following reasons. First of all, if an organization emphasizes the agility of its members, the organization will tend to make a serious effort to

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facilitate the MES use of its members. By its name, Mobile Enterprise Systems (MES) are enterprise systems embedded in mobile hand-held devices with limited computing power, displayed on rather smaller screens than their respective full-version enterprise systems. Thus, they are supposed to be a kind of smaller and more succinct version of their respective enterprise systems. As a result, MES should include only the essential parts of their respective enterprise systems, and GUI (Graphical User Interface) should also be designed such that it is easily accessible and usable for users. Therefore, if MES include only some essential parts of enterprise systems and their GUI has a high level of accessibility, individual users of the MES will feel that they are easier to use, especially compared to their respective enterprise systems, which have a full list of menus and functionalities. In addition, when individual MES users perceive that their organization is an agile organization, requiring their employees to respond quickly to market and demand changes, they will perceive that the MES, which are intended to improve organizational agility, are useful for many parts of their tasks. Therefore, we hypothesize that: H1a. Perceived organizational agility is positively associated with PEU of MES. H1b. Perceived organizational agility is positively associated with PU of MES.

3.2. The impact of users’ location independence As shown in Table 1, a number of studies on the acceptance of mobile IT systems have investigated the impact of an individual’s mobility (e.g., system ubiquity, etc.) as the individual’s internal factor for system acceptance. To list a few, Kim and Garrison [30] found that perceived ubiquity positively influences usage intentions of mobile learning services. Mallat and Rossi [41] also found that mobility positively influences the usage contexts of mobile ticketing services, and Yuan and Archer [69] found that an individual’s mobility is positively associated with the perceived usefulness of mobile work support services. Finally, Schierz and Schilke [53] argued that there is significant variation in the mobility of individual users regarding a technology; they found

Fig. 1. Research model.

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that highly mobile individuals have positive belief about the usefulness of the technology, along with a positive attitude toward and intentions to use the technology in the context of mobile payment services. In the case of MES, which are ‘‘mobile’’ implementations of enterprise software applications, if MES users often work out of fixed locations while using MES, they will perceive the mobile technology as being useful and easy to use for the following reasons. First, since MES are mobile applications, MES will enable users to access to the system ‘‘anytime and anywhere.’’ Thus, as found in previous studies, organizational workers who want to use MES, regardless of their location, find that MES are useful for their tasks. Second, if MES users are often mobile and situated to use MES, regardless of their location, they will use other types of smartphone applications more often than others who work in a fixed location. Thus, they may somehow be accustomed to the functions of many other smartphone applications in general. As a result, since they know how to use smartphone applications in general, MES users with a high level of location independence will perceive their MES as being easy to use, compared with those who are not mobile and have fewer chances to general use mobile applications. With these perspectives, we hypothesize that:

3.4. The impact of attitude toward MES and habitual MES use on perceived job performance gain from MES A positive relationship between attitudes toward using a technology and intentions to use it has been found in a number of studies [53,66]. In this study, we suggest that habitual use can be a good usage measure beyond intentions to use a technology [35,45], and we hypothesize that attitudes toward MES serve as a determinant of habitual MES usage. Remarkably, as noted earlier, the main dependent variable in most studies on TAM is the intentions to use or actual use of a technology [60,61,64]. In this study, we extend TAM into the context of MES by adopting the performance impact of system usage as the main dependent variable. We thus propose that perceived job performance gain from MES can be determined by both attitudes toward MES and habitual MES usage. Accordingly, we present below hypotheses: H5a. Attitude toward MES is positively associated with habitual MES usage. H5b. Attitude toward MES is positively associated with perceived job performance gain from MES.

H2a. A user’s location independence is positively associated with PEU of MES.

H6. Habitual MES usage is positively associated with perceived job performance gain from MES.

H2b. A user’s location independence is positively associated with PU of MES.

3.5. The moderating role of task characteristics

3.3. The impact of perceived ease of use and perceived usefulness Based on the core constructs of TAM [11,59,60], we propose the hypotheses with incorporating additional constructs to extend the original theory. Notably, PEU and PU are the most dominant determinants and belief dimensions for attitude toward systems and use, and PEU has a positive effect on PU [61]. We argue that these relationships among PU, PEU, attitude toward a system, and system use should hold in the context of MES for the following two reasons. First, given the technical limitations of mobile enterprise systems, users’ ease of MES use has become a crucial driver for accepting mobile computing [59]. In particular, specific functionalities such as clear information structure, simplified task procedures, and unlimited accessibility are essential for MES acceptance, as MES being complementary to the established enterprise systems [20]. Second, the fast diffusion and pervasiveness of MES in the workplace could be successful if the MES deliver clear benefits to the users. These benefits, which are reflected by the PU and PEU as well, are regarded as central antecedents to attitudes toward using the technology [11]. In this study, by extending the findings from previous studies on TAM [29,31,41,48,53], we posit that the easier and more intuitive MES are perceived to be, the more useful MES are perceived by users (H3a). Moreover, we propose that both actual habitual MES usage and attitudes toward MES can be explained by perceived ease of use and usefulness of MES (H3b, H3c, H4a, and H4b). H3a. PEU of MES is positively associated with PU of MES. H3b. PEU of MES is positively associated with habitual MES usage. H3c. PEU of MES is positively associated with attitude toward MES.

Since this study investigates the beliefs and behaviors of MES users in organizational environments, the task characteristics of MES users should be important factors for individual users’ performance. Task characteristics have been often investigated as (1) the antecedents of individuals’ beliefs about a technology (e.g., task-technology fit) [16]; 1) the antecedents of individuals’ be2) the antecedents of individuals’ behaviors in organizations (e.g., organizational citizenship behaviors) [57]; or 1) the antecedents of individuals’ be3) the moderating factors for the relationship between individuals’ behavior and their performance [49]. In this study, by taking the theoretical perspective of task characteristics as a contextual factor moderating the relationship between attitude toward MES and perceived job performance, and the relationship between actual MES usage and perceived job performance, we propose that, for the following reasons, both task feedback and task significance are significant factors that moderate those relationships. First, if individual workers perceive that their degree of task feedback is high and that they need to obtain direct and clear information about the effectiveness of their performance [17], their tasks require a great deal of feedback not only from co-workers, but also from various information sources that are intended to inform how well they perform (e.g., sales representatives need on-time information about sales records, inventory status, and other performance indicators from CRM, which is a functional part of an enterprise system). In this situation, attitudes toward MES and habitual MES use will help improve performance more strongly than tasks requiring a lesser degree of feedback, because the MES promptly provide various forms of feedback from both human information sources (normally through the mobile Intranet) and non-human sources (normally performance indicators embedded in enterprise applications). Combining these perspectives, we hypothesize that: H7a. Task feedback positively moderates the relationship between attitude toward MES and perceived job performance gain from MES.

H4a. PU of MES is positively associated with attitude toward MES. H4b. PU of MES is positively associated with habitual MES usage.

H7b. Task feedback positively moderates the relationship between habitual MES usage and perceived job performance gain from MES.

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Second, as stated above, MES are provided for organizational workers to improve their ability to cope with internal and external changes in market and demand. Thus, embedded in hand-held devices (e.g., smartphones), the MES provide organizational workers with significant information about a great deal of operational and performance indicators so that the workers can access the information anytime and anywhere. Thus, when MES users feel that their tasks have a substantial impact on the wellbeing of their organization itself, as well as on the well-being of other members in the organization [17], both their attitudes toward the MES and actual MES usage will have a stronger impact on MES users’ performance than when MES users do not feel that their tasks are very crucial for the overall well-being of the organization. This is because those individuals with a high level of task significance will perceive the accessibility to MES anytime and anywhere as beneficial to improving their work performance significantly. With these perspectives, we hypothesize that: H8a. Task significance positively moderates the relationship between attitude toward MES and perceived job performance gain from MES. H8b. Task significance positively moderates the relationship between habitual MES usage and perceived job performance gain from MES.

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research team. First, we e-mailed the panels a direct link to the electronic questionnaire, soliciting their participation. A few days later, we e-mailed them several formal notifications in order to encourage them to respond to the electronic questionnaire. In order to collect survey data from only the participants with practical experience in using MES in their workplaces, we asked the following items at the beginning of the questionnaire: ‘‘Please indicate all mobile devices that you are currently using’’; ‘‘Does your company provide mobile enterprise systems for your work?’’ and, ‘‘Do you have experience in e-mailing, approving, or documenting through mobile systems that were provided by your company?’’ By using these screening items, only participants with experience in using MES administrated by their company could proceed to the subsequent questions. We conducted the survey from January 15, 2013 to March 30, 2013 and total 2,214 people were solicited for the survey. After preliminary questions which were aimed to screen participants with practical experience in MES in their workplace (i.e., a worker who has an experience with MES through their smartphone or tablet PC), only 403 participants were passed and fitted to our criteria, which resulted in an 18.2% valid response rate. Since online survey tool automatically protected missing items throughout web screen, 403 participants fully answered a questionnaire. Table 2 shows the respondents’ characteristics, according to their work experience and demographics. The sample comprised 48.1% women and 51.9% men, and the 6–12 months work experience with mobile enterprise systems

4. Research methodology 4.1. Measurement We modified the extant measures in order to fit them into the MES, or we developed new items if the extant measures did not exist, to test our research model. Before conducting a survey, we had conducted pilot tests with three academics and two practitioners who are experts in information systems, and we revised the modified measures, according to their feedback. The measures included in the questionnaires were developed using seven-point Likert scales (with scale item responses ranging from 1 = ‘‘strongly disagree’’ to 7 = ‘‘strongly agree’’ or from 1 = ‘‘not at all’’ to 7 = ‘‘to a great extent’’). We took the survey items for perceived job performance gain from MES from previous studies [4,16,26,43,51]. Based on these items, we examined individuals’ perceived job performance through MES usage, by measuring (1) successful use of MES for their work-related activities, (2) satisfactions with MES as a tool for efficient job fulfillment, and (3) achieved efficiency in task fulfillment using MES. These three dimensions of individuals’ perceived job performance gain from MES can capture the overall benefits (or performance impacts) of MES. We adopted the items associated with task characteristics (i.e., feedback and significance) from the Job Characteristic Inventory (JCI) [54]. Based on previous work [35], we modified the scales for habitual use to fit into the context of MES. We adopted the items for attitude toward MES from a previous study [3], the items for PEU and PU from the original TAM [11,61], and the items for perceived organizational agility from previous studies [15,37]. Finally, we developed the scales for location independence based on the work of previous studies [30,41,53]. The specific survey items and their sources are listed in Appendix A.

Table 2 Demographic characteristics of respondents (N = 403).

Gender Age

Position

Education

Work experience

Work experience with mobile enterprise systems

Number of employee

Industry

4.2. Sample and survey administration We conducted a field survey among actual MES users in their workplace. Organizational workers who actually use MES in their workplaces were recruited as panels, and they participated in an online questionnaire survey, which was administered by the

Items

Frequency

Percentage

Male Female 21–30 31–40 41–50 Above 51 Manager Middle manager Senior manager Executive High school College (two year) Bachelor (four year) Graduate Below 2 years 2–5 years 6–10 years 11–15 years 16–20 years Above 21 years Below 3 months

209 194 95 129 114 65 216 82 86 19 37 55 275 36 20 71 102 82 75 53 35

51.9 48.1 23.6 32.0 28.3 16.1 53.6 20.3 21.3 4.7 9.2 13.6 68.2 8.9 5.0 17.6 25.3 20.3 18.6 13.2 8.7

3–6 months 6–12 months 1–2 years Above 2 years Under 50 people 51–100 people 101–500 people 501–1000 people Above 1001 people Manufacturing Service Hospital Government Information technology Finance Education Others

91 125 109 43 98 78 73 46 108 85 98 17 28 59 24 47 43

22.6 31.0 27.0 10.7 24.3 19.4 18.1 11.4 26.8 21.2 24.4 4.2 7.0 14.7 6.0 11.7 10.7

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comprised the largest proportion of respondents (31.0%), followed by 1–2 years (27.0%). 5. Research results We employed Partial Least Squares (PLS) analysis with SmartPLS [50] to test our structural model. PLS is appropriate for the structural model test in this study, because our model contains multi-paths and non-normal data [6]. We performed Kolmogorov–Smirnov and Shapiro–Wilk tests to examine the normality of distributions by using SPSS Version 20.0, and found that the sample distributions from our data did not obey the normal distribution. Also, PLS is appropriate to test the moderation effect by measuring the level of significance of the interaction terms and calculating the moderation effect size [8]. 5.1. Testing the measurement model We conducted a confirmatory factor analysis to assess the reliability and validity of the measurement model. First, as shown in Table 3, the smallest value of Cronbach’s a is 0.80, indicating a satisfactory level of internal reliability of the measurement item. Second, for convergent validity, item loadings exceed the recommended threshold of 0.6 [19]. Third, for the reliability, the composite reliability (CR) measures of all latent variables exceed the recommended threshold of 0.7, and the average variance extracted (AVE) values for each construct exceed 0.50 [14]. Fourth, for discriminant validity, the AVE for a construct exceeds the variance shared between the construct and other constructs in the model [7]. Table 4 shows the inter-construct correlations, with the square roots of the AVE of each construct in the diagonal elements. The square

roots of the AVE exceed the inter-construct correlations, thereby satisfying the discriminant validity. Lastly, to minimize common method bias in self-reported survey research, we included a common method factor in the model and evaluated each indicator’s variance substantively explained by the principal construct and the method factor [34]. The results in Table 5 show that the average variance of the constructs’ indicator is 0.755, whereas the average method-based variance is 0.008. Given the small magnitude and insignificance of the method variance, we conclude that the systematic error from the method bias is not a serious concern. 5.2. Testing the structured model We measured the explained variance (R2) of the dependent and mediating variables, path coefficients (b), and their levels of significance (t-values), which were obtained from a bootstrapping with re-sampling (810 re-samples, greater than 2 times our sample size = 403) to assess the significance of the hypothesized relationships. The following Fig. 2 depicts the explained variances (R2), the structural path-coefficient estimates on each path (b) and their levels of significance (based on t-values), along with the moderation effect sizes. All hypotheses, except H2a and H8a, are supported at the a = 0.15, 0.1, 0.05 or 0.01 levels of significance. First, perceived organizational agility is positively associated with the PEU of MES (H1a: b = 0.428, t = 8.544) and is also positively associated with the PU of MES (H1b: b = 0.294, t = 5.178). However, the location independence of an MES user is positively associated only with the PEU of MES (H2a: b = 0.113, t = 2.232), and is not associated with the PU of MES (H2b, not supported). The results from the test of H1 indicate that, as hypothesized, if individual workers perceive that their organizational environment requires market- and operation-related agility, then they will find

Table 3 Scale reliabilities and convergent validity. Construct

Cronbach’s a

CR

AVE

Item

Factor loading

Mean

SD

Perceived job performance gain from MES

0.85

0.91

0.77

Task feedback

0.81

0.87

0.70

Task significance

0.80

0.88

0.71

Attitude toward MES

0.90

0.92

0.75

Habitual use

0.88

0.92

0.80

Perceived ease of use

0.88

0.93

0.81

Perceived usefulness

0.92

0.94

0.81

Organizational agility

0.93

0.94

0.73

Location independence

0.80

0.88

0.72

Perfo1 Perfo2 Perfo3 TF1 TF2 TF3 TS1 TS2 TS3 ATT1 ATT2 ATT3 ATT4 HU1 HU2 HU3 HU4 PEU1 PEU2 PEU3 PU1 PU2 PU3 PU4 OA1 OA2 OA3 OA4 OA5 OA6 LI1 LI2 LI3

0.87 0.90 0.86 0.81 0.88 0.81 0.85 0.82 0.85 0.81 0.89 0.91 0.86 0.90 0.89 0.85 0.90 0.88 0.89 0.92 0.88 0.90 0.91 0.92 0.81 0.88 0.85 0.88 0.85 0.87 0.86 0.83 0.84

5.03 5.05 5.14 4.97 5.11 5.07 5.27 5.21 5.07 4.90 4.49 4.55 4.53 4.70 4.53 4.47 4.70 4.88 4.69 4.87 5.08 4.99 5.02 5.13 4.92 4.85 4.90 4.96 4.94 4.87 4.52 4.19 4.36

1.13 1.10 1.19 1.04 1.03 1.01 1.02 1.12 1.15 1.13 1.26 1.26 1.35 1.12 1.16 1.25 1.12 1.13 1.04 1.09 1.09 1.10 1.08 1.08 1.12 1.12 1.12 1.15 1.17 1.11 1.44 1.48 1.44

Notes: CR: composite reliability; AVE: average variance extracted, SD: standard deviation.

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Table 4 Correlation matrix and discriminant validity assessment. 1 1. Perceived job performance gain from MES 2. Task feedback 3. Task significance 4. Attitude toward MES 5. Habitual use 6. Perceived ease of use 7. Perceived usefulness 8. Organizational agility 9. Location independence

2

3

4

5

6

7

8

9

0.833 0.658 0.275 0.450 0.428 0.456 0.611 0.214

0.838 0.234 0.370 0.384 0.428 0.584 0.186

0.867 0.483 0.424 0.481 0.341 0.252

0.897 0.526 0.566 0.518 0.402

0.898 0.584 0.463 0.247

0.901 0.506 0.223

0.856 0.313

0.845

0.874 0.449 0.424 0.461 0.581 0.547 0.652 0.489 0.260

Notes: The bold numbers on the diagonal are the square roots of the AVEs. The off-diagonal numbers are the intercorrelations among constructs.

MES useful and easy to use for their work. The results from the test of H2a indicate that individuals with a high level of location independence will perceive MES as being easy to use because they often use other mobile applications while they are in motion, which helps them become more easily accustomed to using MES. The results from testing H2b, however, indicate that, although individual workers may feel independent of their locations when using MES, they may not necessarily feel that MES are very useful for their work. This insignificant relationship implies that PU of MES originates from the usefulness of its respective enterprise system itself, rather than the fact that the enterprise system is realized in a mobile hand-held device. Second, as hypothesized, in the context of MES as well, PEU of MES is positively associated with PU of MES (H3a: b = 0.442, t = 8.293),

habitual MES usage (H3b: b = 0.244, t = 4.258), and attitude toward MES (H3c: b = 0.217, t = 3.148). PU of MES is positively associated with both attitude toward MES (H4a: b = 0.355, t = 5.059) and habitual MES usage (H4b: b = 0.312, t = 5.948). The results from testing H3 and H4 reconfirm that TAM holds in the context of MES, as well. Third, attitude toward MES is associated not only with habitual MES usage (H5a: b = 0.228, t = 4.688) but also with individual workers’ perceived performance with MES (H5b: b = 0.240, t = 5.266). As well, habitual usage is positively associated with perceived performance with MES (H6: b = 0.449, t = 9.318). These results indicate that if individual workers have positive attitudes toward an MES, they will tend to use it more habitually and eventually, they will tend to perceive their performance as being improved by using the MES.

Table 5 Common method bias analysis. Construct

Item

Substantive factor loading (R1)

R12

Method factor loading (R2)

R22

Perceived job performance gain from MES

Perfo1 Perfo2 Perfo3 TF1 TF2 TF3 TS1 TS2 TS3 ATT1 ATT2 ATT3 ATT4 HU1 HU2 HU3 HU4 PEU1 PEU2 PEU3 PU1 PU2 PU3 PU4 OA1 OA2 OA3 OA4 OA5 OA6 LI1 LI2 LI3

0.866*** 0.903*** 0.856*** 0.806*** 0.883*** 0.807*** 0.847*** 0.819*** 0.848*** 0.807*** 0.888*** 0.905*** 0.863*** 0.902*** 0.923*** 0.865*** 0.903*** 0.879*** 0.894*** 0.921*** 0.878*** 0.898*** 0.905*** 0.919*** 0.813*** 0.878*** 0.852*** 0.878*** 0.849*** 0.867*** 0.863*** 0.828*** 0.843***

0.750 0.815 0.733 0.650 0.780 0.651 0.717 0.671 0.719 0.651 0.789 0.819 0.745 0.814 0.852 0.748 0.815 0.773 0.799 0.848 0.771 0.806 0.819 0.845 0.661 0.771 0.726 0.771 0.721 0.752 0.745 0.686 0.711

0.003 0.017 0.015 0.041 0.124*** 0.091* 0.030 0.033 0.001 0.282*** 0.090** 0.070** 0.097*** 0.152** 0.058* 0.100** 0.285*** 0.025 0.010 0.014 0.021 0.001 0.034 0.014 0.029 0.030 0.103* 0.005 0.022 0.086 0.039 0.069** 0.030

0.000 0.000 0.000 0.002 0.015 0.008 0.001 0.001 0.000 0.080 0.008 0.005 0.009 0.023 0.003 0.010 0.081 0.001 0.000 0.000 0.000 0.000 0.001 0.000 0.001 0.001 0.011 0.000 0.000 0.007 0.002 0.005 0.001

0.868

0.755

0.009

0.008

Task feedback

Task significance

Attitude toward MES

Habitual use

Perceived ease of use

Perceived usefulness

Organizational agility

Location independence

Average * **

p < 0.05. p < 0.01. p < 0.001.

***

614

S. Chung et al. / Information & Management 51 (2014) 605–617

Fig. 2. Research results.

Fourth, in order to test the moderation effects of task feedback and task significance (H7a–H8b), we adopted the procedure that Chin et al. [8] introduced: we measured the t-statistics of the interaction factors (moderator  main effect variable) and calculated the effect sizes using the R2s of the two models: (1) the full model with both a moderating variable (as an independent variable) and an interaction term (moderator  main effect variable) on the predicted variable; and (2) the partial model with only the moderating variable as an independent variable on the predicted variable in the PLS model [8]. H7a, the moderating effect of task feedback on the relationship between attitude and perceived performance, is marginally supported at the a = 0.15 level (t = 1.436), with a negligible effect size of 0.7%. H7b, the moderating effect of task feedback on the relationship between habitual MES usage and perceived performance, is supported at the a = 0.05 level (t = 2.148), with a small, but not negligible effect size of 1.8% [21]. H8a, the moderating effect of task significance on the relationship between attitude and perceived performance, is not supported (t = 0.763), with hardly any effect size. H8b, the moderating effect of task significance on the relationship between habitual MES usage and perceived performance, is supported at the a = 0.1 level (t = 1.733), with a very small effect size of 1.3%. The results from these moderating effect tests indicate that, even with the existence of the strong main effects of habitual usage and positive attitude (R 2 = 0.400 means that approximately 40% of the variance in performance is explained by main effect and control variables), task characteristics (i.e., task feedback and task significance) slightly, but significantly improve the impact of habitual usage on perceived performance. These results also indicate that task characteristics moderate the impact of usage on performance more strongly than the impact of positive attitude on performance. Finally, among six control variables that we added in our research model, the experience of using MES (b = 0.107, t = 2.567) and the level of education (b = 0.097, t = 2.008) are significant factors for performance at the a = 0.01 and 0.05 levels, respectively, while the rest are found to be insignificant for performance. Overall, approximately 40.0% of the variance in perceived performance with MES is explained by our research model.

6. Discussions 6.1. Implications for theory The goals of this study are twofold: (1) to explore the role of two important task-carried contexts of MES users: (a) location independence as an individual’s internal factor; and (b) perceived organizational agility as an individual’s external factor for the technology acceptance model of an MES, as well as (2) to extend the technology acceptance model for MES by adding (a) an individual’s perceived performance as the consequence of positive attitude and system usage; and (b) task characteristics (task feedback and significance) as moderating factors on the relationship between usage (attitude) and performance. To achieve these goals, we developed an extended technology acceptance model to theorize and test the above-mentioned relationships. With 403 surveys collected from organizational workers in various industries, we empirically validated that perceived organizational agility is positively associated with both PU and PEU, that users’ location independence is positively associated with PEU, and that two task characteristics (feedback and significance) positively moderate the impact of habitual MES use on perceived performance. We also tested our extended technology acceptance model in the context of MES usage by showing significant relationships among PU, PEU, attitude, habitual usage, and performance of individual MES users. More specifically, this study has several implications for theory in the following ways. First, as task-carried context variables [36], we introduced individual workers’ perceived organizational agility and location independence as key antecedents for beliefs about MES, a mobile groupware intended to improve organizational agility. Although organizational agility has been conceptualized and investigated at the organizational and team levels [32,37], it can be interpreted as individual organizational members’ perceptions regarding their task-carried contexts, which can be an antecedent for their beliefs about an IT system. By providing empirical evidence that an individual’s perceived organizational agility is positively associated with both PU and PEU of MES, this study contributes to the body of knowledge concerning the theory of technology acceptance in the context of enterprise systems, and concerning the theory of

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organizational agility. Also, we introduced the concept of individuals’ location independence, which embraces the meanings of ‘‘the mobility of users’’ and ‘‘the ubiquity of technology.’’ We found a positive relationship between location independence and PEU, which also contributes to the theory of TAM in the context of mobile services (devices). These two variables can be further used in future research on mobile groupware for organizational workers. Second, we treated task characteristics as important moderating factors for the effect of MES usage on performance. We identified salient task characteristics (feedback and significance) in the context of MES, and found that the interactions of these task factors and habitual MES use significantly influence individuals’ performance. We also found that under a high level of task feedback and significance, the impact of habitual MES usage on individual performance becomes stronger. With respect to the theory, our findings imply that task characteristics can be a good moderating factor for the relationship between system usage and performance in the context of mobile applications for organizational workers. Based on our findings, other relevant task characteristics for various mobile applications can be investigated as important contextual factors for the acceptance of technologies and performance impact from using technologies. Third, by extending TAM with both new sets of antecedents (agility and location independence) and consequences (individual performances), we found that, in the context of MES as well, there exist significant links among those antecedents, PU, PEU, attitude, habitual usage, and perceived job performance gain from MES. Especially, in order to investigate the performance impact of MES usage, instead of using behavioral intentions to use an MES, we included habitual usage in our research model. In the context of mobile applications, the habitual usage construct should be more relevant than behavioral intentions, because in the case of MES embedded in mobile hand-held devices, an individual’s usage of MES can be easily habitualized by the user [45] so that habitual MES usage can reflect an individual’s actual use of the application. Limayem et al. [35] also argued that habitual use is associated with individuals’ perceived frequency and comprehensiveness of system use. The significant relationships among beliefs about the systems (PU and PEU), habitual use, and perceived job performance gain from MES found in this study imply that habitual usage can be a good measure of actual usage behavior and that such habitual usage can be used to link beliefs about a technology and performance by using the technology in a mobile context. Thus, in future research, habitual usage can be further used as an actual usage measure for the applications (services) embedded in mobile hand-held devices. Fourth, our data were collected from organizational workers at multiple organizations in multiple industries. Also, to improve our data quality, we screened out some of our initial survey targets so as to gather the survey data from only organizational workers who have used MES for a certain period of time for their work-related activities, which nevertheless resulted in a large sample (n > 400). Thus, the empirical results of this study can be more generalizable than other empirical studies relying on a small dataset gathered from a single organization or single industry. 6.2. Implications for practice The findings of this study provide a number of important implications for practitioners in their implementation and use of MES to improve employees’ job performance in the workplace. First, our findings suggest that employees who work within an agile organizational environment and require anytime/anywhere access to enterprise systems have positive beliefs about MES and eventually get to use MES more habitually. This finding explains

615

why many contemporary organizations need to implement MES in order to confront a rapidly changing market and to meet the requirements of employees who are always in motion. Second, the results from our empirical study with data gathered from various industries show the positive impact of positive attitudes and habitual MES use on employees’ perceived job performance gain from MES. This finding implies that the implementation and facilitation of MES for employees actually improve their performance in contemporary organizations within various industries. In addition, the moderating impacts of two task characteristics (feedback and significance) suggest that if MES users need to exchange a great deal of just-in-time feedback about their performance, positive attitudes toward and habitual MES usage can make a significant difference, and can even enhance their job performance. Also, if MES users perform significant tasks that influence other organizational members’ well-being, their MES usage should have a stronger impact on performance. This result simply shows that MES is an important system for the overall well-being of contemporary organizations. More specifically, it implies that MES usage should be more intensively facilitated for employees (1) whose task fulfillment and decisionmaking have a greater impact on the overall well-being of an organization; and (2) who usually assume higher hierarchical positions. According to our results, when firms implement MES, they should first confirm whether their employees often need to be relocated, and whether they need to have mobile access to enterprise systems for their jobs. If so, firms need to build organizational agility to confront market and demand changes with the help of IT in order to take advantage of MES implementation and to better facilitate MES usage by employees. Also, they should check individual employees’ task characteristics in terms of feedback and significance. MES facilitators in an organization should start with those whose tasks have a high degree of feedback and significance, in order to maximize the impact of MES implementation on employees’ performance. 6.3. Limitations This study has certain limitations and provides directions for future research. First, this study investigates organizational agility as an antecedent (organizational environment factor) of individual MES acceptance, but agility at the individual level (i.e., perceived improvement in personal agility) could be a consequence of MES usage. That is, individuals who habitually use MES may improve their ability to rapidly confront various issues about market and demand changes. Thus, in future research, it would be interesting to look into the impact of MES usage on the improvement of individual agility. Further, as agility has been more frequently studied at the collective level, it would be worthwhile to investigate and compare the relationships between the MES implementation in an organization and the agility of the organization, at the different units of organizations such as teams, departments, or firms. Second, the insignificant relationship between location independence and perceived usefulness needs further investigation. For example, since we mentioned that the perceived usefulness about MES is already influenced by the usefulness of the full enterprise systems, it would be interesting to examine the interrelationships among mobile workers’ location independence, the PU of MES and also the PU of the respective full version of enterprise systems involving the particular MES. Finally, we collected our survey data via a cross-sectional approach with self-reported measures of respondents’ perceptions of the constructs. This cross-sectional survey design may prohibit the conclusion of causality. With this design, we may only

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conclude that the significant relationships among the antecedents, beliefs, attitudes, usage, and performance could be associative, and not causal. Also, self-reported performance measures could create a self-serving bias in the respondents, although we try to overcome this bias by collecting over 400 surveys from the respondents in various industries. Therefore, in order to address the causality issue and the self-serving bias, it is important to conduct future studies with objective data, possibly with a longitudinal approach.

HU3: When I start to work, I visit the mobile enterprise systems firstly. Perceived Ease of Use [11,61] PEU1: It is easy to use the mobile enterprise systems. PEU2: It is easy to get the mobile enterprise systems to do what I want it to do. PEU3: It is convenient to access the mobile enterprise systems.

7. Conclusion Our model provides a new set of antecedents, moderating factors and consequence variables for a TAM involving mobile applications. The research model also provides empirical evidence that MES are important mobile services that positively influence individuals’ performance. Our findings suggest that perceived organizational agility does have a significant influence on MES usage through positive beliefs about MES, that individuals’ location independence can also influence individuals’ beliefs about a technology (ease of use), and that task factors are important contingent factors for the impact of system usage and perceived job performance gain from MES. Overall, this study strengthens our knowledge regarding the impact of mobile applications in organizational contexts, technology acceptance and the role of task characteristics. Appendix A Measurement Items

Perceived Job Performance gain from MES [4,16,26,43,51] Perfo1: I successfully use the mobile enterprise systems to perform my job. Perfo2: I am satisfied with the effect of using mobile enterprise systems on my job performance. Perfo3: Using mobile enterprise systems helps reduce the lead time of performing the job tasks.

Perceived Usefulness [11,61] PU1: Using mobile enterprise systems enable me to accomplish tasks more quickly. PU2: Using mobile enterprise systems enhances my task effectiveness. PU3: Using mobile enterprise systems makes it easier to do my task. PU4: The mobile enterprise systems are useful in performing my task. Organizational Agility [15,37] OA1: We fulfill demands for rapid-response, special requests of our customers whenever such demands arise; our customers have confidence in our ability. OA2: We can quickly scale up or scale down our production/service levels to support fluctuations in demand from the market. OA3: Whenever there is a disruption in supply from our suppliers we can quickly make necessary alternative arrangements and internal adjustments. OA4: We are quick to make and implement appropriate decisions in the face of market/customer-changes. OA5: We constantly look for ways to reinvent/reengineer our organization to better serve our market place. OA6: We treat market-related changes and apparent chaos as opportunities to capitalize quickly. Location Independence (items developed) LI1: I have to move frequently to my job.

Task Feedback [54]

LI2: I spend more time outside of company than office.

TF1: My job provides feedback on how well I am doing as I am working.

LI3: Using MES for My job is uninhibited for space.

TF2: My job enables me to find out how well I am doing. TF3: My job provides me with the feeling that I know whether I am performing well or poorly. Task Significance [54] TS1: My job is relatively significant in my organization. TS2: My job is important in the broader scheme of things. TS3: My job is one where a lot of other people can be affected by how well the work gets done. Attitude toward MES [3] How do you feel about your overall experience of mobile enterprise systems use: ATT1: Very dissatisfied/Very satisfied ATT2: Very displeased/Very pleased ATT3: Very frustrated/Very contented ATT4: Absolutely terrible/Absolutely delighted Habitual Use [35] HU1: Using the mobile enterprise systems has become a habitual act. HU2: I use the mobile enterprise systems automatically.

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