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Task−Technology Fit for Mobile Information Systems Judith Gebauer University of Illinois at Urbana−Champaign

Michael J. Shaw

Michele L. Gribbins

University of Illinois at Urbana−Champaign

University of Illinois at Urbana−Champaign

Abstract Mobile information systems (IS) hold great promise to support organizational processes. Clear guidelines, however, of how to design effective mobile IS in support of organizational processes have not been developed. Based on earlier research that emphasizes the importance of fit between organizational tasks and technology and that develops fit profiles for specific task−technology combinations, this paper develops a task−technology fit (TTF) profile for mobile IS to support managerial tasks. We suggest a three−way match between dimensions of managerial tasks, mobile IS, and the mobile use context. We find that use situations characterized by high distraction and poor quality of network connection are particularly challenging for the design of mobile IS, and that the user interface requires particular attention. The proposed conceptual model of task−technology fit provides guidelines for the design of effective mobile IS and for future research studies.

This working paper replaces 2005 Working Paper #05−0119 Published: 6/26/2006 URL: http://www.business.uiuc.edu/Working_Papers/papers/06−0107.pdf

Task−Technology Fit for Mobile Information Systems Judith Gebauer University of Illinois at Urbana−Champaign

Michael J. Shaw

Michele L. Gribbins

University of Illinois at Urbana−Champaign

University of Illinois at Urbana−Champaign

Abstract Mobile information systems (IS) hold great promise to support organizational processes. Clear guidelines, however, of how to design effective mobile IS in support of organizational processes have not been developed. Based on earlier research that emphasizes the importance of fit between organizational tasks and technology and that develops fit profiles for specific task−technology combinations, this paper develops a task−technology fit (TTF) profile for mobile IS to support managerial tasks. We suggest a three−way match between dimensions of managerial tasks, mobile IS, and the mobile use context. We find that use situations characterized by high distraction and poor quality of network connection are particularly challenging for the design of mobile IS, and that the user interface requires particular attention. The proposed conceptual model of task−technology fit provides guidelines for the design of effective mobile IS and for future research studies.

Published: 6/26/2006 Entered: June 26, 2006.

Task-Technology Fit for Mobile Information Systems

Judith Gebauer, Michael J. Shaw, Michele L. Gribbins {gebauer|mjshaw|mgribbin}@uiuc.edu University of Illinois at Urbana-Champaign College of Business Department of Business Administration 350 Wohlers Hall 1206 South Sixth Street Champaign, IL 61820

Last updated: June 21, 2006

Abstract Mobile information systems (IS) hold great promise to support organizational processes. Clear guidelines, however, of how to design effective mobile IS in support of organizational processes have not been developed. Based on earlier research that emphasizes the importance of fit between organizational tasks and technology and that develops fit profiles for specific task-technology combinations, this paper develops a task-technology fit (TTF) profile for mobile IS to support managerial tasks. We suggest a three-way match between dimensions of managerial tasks, mobile IS, and the mobile use context. We find that use situations characterized by high distraction and poor quality of network connection are particularly challenging for the design of mobile IS, and that the user interface requires particular attention. The proposed conceptual model of task-technology fit provides guidelines for the design of effective mobile IS and for future research studies. Keywords: Mobile information systems, managerial tasks, task-technology fit

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Motivation The relationships among technology, organizational processes, and performance are of great interest to organizational researchers (Orlikowski 2000). Technologies now exist that enable employees to “stay close to their local situations while engaging in global activities critical to their company’s sustainability (Malhotra and Majchrazk 2005).” One such technology is mobile information systems (IS). The ubiquitous nature of mobile IS provide new opportunities, issues and challenges (Lyytinnen and Yoo 2002a, 2002b) to organizations as they adopt these new technologies into their processes with the hopes of enhancing performance. While mobile IS that are deployed to support an increasingly mobile workforce promise to improve organizational processes (Balasubramaniam, Peterson, and Jarvenpaa 2002; Computerworld 2003), many questions remain concerning technology development, applications and business models (Agrawal, Chari, and Sankar 2003; Smith, Kulatilaka, and Venkatraman 2002; Tarasewich, Nickerson, and Warkentin 2002; Zhang, Yuan, and Archer 2003).

In particular, the

requirements of mobile IS to adequately support mobile professionals have not been identified systematically. This paper integrates earlier research in the areas of organizational tasks, mobile technology, and task-technology fit in order to develop a profile of task-technology fit (TTF) for mobile IS, with the intent to contribute to the effectiveness and success of mobile IS in organizational settings. More specifically, we hope to identify areas where the deployment of mobile IS can be considered particularly promising or difficult to achieve due to the use context. Our results help assess and explain the success of mobile IS applications within organizations, while providing conceptual guidelines for system development. In addition, our systematically derived propositions comprise a research framework that can guide future research studies. In the following, we first discuss our conceptual bases of earlier research publications of task-technology fit, managerial tasks and mobile IS. We then develop a profile of task-technology fit for mobile IS, discuss the implications of our propositions, draw a number of conclusions, and point out avenues for future research.

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Two Theories of Task-Technology Fit Two largely independent theories of TTF have emerged. The first, initiated by Goodhue and Thompson (1995), established TTF as an important concept in assessing and explaining IS success. The second, initiated by Zigurs and Buckland (1998), developed a systematic profile for the task-technology combination of group tasks and group support systems (GSS). While Goodhue and Thompson (1995) focused on individuals’ use of IS and presented a primarily positivistic research approach applicable to IS in general, Zigurs and Buckland (1998) focused on groups’ use of IS and formulated fit profile applicable specifically to GSS. Both streams are reviewed next. Task-Technology Fit to Explain IS Success Goodhue and Thompson (1995) proposed a comprehensive technology-to-performance model that included characteristics of information technology, tasks, and of the individual user as explanatory variables for technology use and for individual performance. A simpler version of the technology-toperformance model, referred to as the TTF model, found moderate empirical support for the direct links between task and technology characteristics and user-perceived TTF. Results confirmed that TTF and usage together better explained the impact of information technology on individual performance (i.e., user-perceived accomplishment of individual tasks) than usage alone. Related studies broadly confirmed the relevance of the TTF construct to assess the value of an IS (Goodhue 1995) and to assess and predict system usage (Dishaw and Strong 1998) and individual performance (Goodhue et al. 2000). Staples and Seddon (2004) confirmed that the technology-toperformance model can explain performance for both mandatory and voluntary use settings. Different aspects of TTF have been confirmed relevant for IS in general (Ferratt and Vlahos 1998, Goodhue 1995, Goodhue 1998, Goodhue et al. 1997, and Goodhue and Thompson 1995), as well as for specific technologies (Dishaw and Strong 1998, 1999; Goodhue et al. 2000), and for a variety of tasks (Dishaw and Strong 1998, 1999; Ferrat and Vlahos 1998; Goodhue 1995, 1998; Goodhue and Thompson 1995; Goodhue et al. 1997, 2000; Majchrzak, Malhotra and John 2005; Staples and Seddon 2004).

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In summary, we note that this stream of research corroborated the relevance of the TTF concept in explaining and predicting IS success for individual performance. Since no systematic bias has been identified regarding the relevance of TTF for different types of IS, we assume that TTF is a valid construct to explain the success of mobile IS, yet we also take note of the need to include into the analysis the particularities of mobile technology as compared to non-mobile technology, such as the individual use context. The basic idea of TTF has been considered in mobile IS research studies (Gebauer and Shaw 2004, Junglas and Watson 2003, Liang and Wei 2004), but has not been integrated systematically. A limiting aspect to our research objective is the fact that Goodhue and Thompson (1995) focused more on the relevance of the TTF concept to explain individual performance. The systematic analysis of requirements to achieve fit for particular combinations of tasks and technology, however, achieved less attention, an aspect that is addressed by the second stream of research. Task-Technology Fit for Group Support Systems Building on earlier research work on organization and group processes, Zigurs and Buckland (1998) developed a theory of TTF to support the development and deployment of GSS to support group tasks. Assuming that a good fit between tasks and technology would result in good group performance, the authors defined fit as “ideal profiles composed of an internally consistent set of task contingencies and GSS elements that affect group performance (p. 323).” Performance was viewed generically as the accomplishment of group goals to be operationalized for individual task situations. Five categories of group tasks were identified (simple, problem, decision, judgment, and fuzzy) as well as three technology support dimensions (communication, process structuring, information processing support). Finally, a set of fit profiles was developed (e.g., “Simple tasks should result in the best group performance … when done using a GSS configuration that emphasizes communication support”), which was later tested and largely confirmed by Zigurs et al. (1999). Research studies building on Zigurs and Buckland’s (1998) theory of TTF generally sought to improve the support of collaborative and group tasks to be conducted in various circumstances, with a variety of group support technologies (Barkhi 2001-2002, Dennis, Wixom and Vandenberg 2001,

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Massey, Montoya-Weiss, Hung and Ramesh 2001, Murty and Kerr 2004, Sussman, Gray, Perry and Blair 2003). Jahng, Jain and Ramamurthy (2000) applied a similar concept in the area of electronic commerce, but overall, Zigurs and Buckland’s (1998) theory of TTF has mostly been applied to collaborative technologies. Research has found that not all tasks that a group might undertake are best supported by collaborative technologies (see Malhotra and Majchrzak 2004; Wittenbaum, Hollingshead and Botero 2004). Towards a Profile of TTF for Mobile IS To develop a profile of TTF for mobile IS, we apply the concept of task-technology fit to managerial tasks, supported by IS in a mobile use context, using task performance as a proxy of system success. Similar to Zigurs and Buckland (1998), we consider TTF as pre-defined profiles, which we develop in a three step process. Profiles have the benefit of being specific about the suggested areas of application and the identification of potential problems while outlining a research agenda. We first look at the main constructs and then describe the steps to derive TTF for mobile IS. Managerial tasks As managers are among the most mobile employees in organizations and many current applications of mobile technology do in fact target managers (Computerworld 2003), a focus on managerial tasks is warranted for the analysis of mobile IS. Managerial research studies have frequently used two dimensions to describe managerial tasks: task non-routineness (ranging from low to high) and task interdependence (ranging from low to high). In the context of mobile IS, a third dimension, timecriticality (ranging from low to high), appears to be relevant. Task Non-Routineness The concept of task non-routineness has a long history in management research. Anthony (1965) categorized managerial activities based on the degree of (non-)routineness (operational control, managerial control, strategic planning), followed by Gorry and Scott Morton (1971) who linked the degree of structure with different organizational levels of managerial decision making and stated that lower degrees of structure (routineness) are associated with higher levels of management. In an analysis

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of how humans solve problems, Simon (1960) found the level of structure (routineness) of a task to be manifested in characteristics such as repetitiveness and novelty, and to determine the ease with which managerial decision making can be programmed (automated) or requires judgment and intelligent, adaptive, problem-oriented action. Perrow (1967) described organizational technologies according to the number of exceptions to be handled and the degree to which a search procedure is analyzable (i.e., relying on past experiences and previously developed concepts and routines) or unanalyzable (i.e., not logical or unanalytic, often reverting to intuition, chance, and guesswork). According to Perrow (1967), non-routine technology is best applied to situations with large numbers of exceptions and unanalyzable search results, while routine technology is best applied to situations with few exceptions and analyzable search results. Van de Ven and Ferry (1980) distinguished between two dimensions of task structure: task variability (e.g., number of exceptions), and task difficulty (e.g., analyzability and predictability). Ahuja and Carley (1999) classify tasks by their analyzability and variety, defining task analyzability as “the extent to which a task can be broken down into small, well-defined components” and task variety as “the extent to which there is variation in the task over time.” Since in practice, task variety and difficulty (analyzability) were correlated and difficult to distinguish, some researchers have combined the two variables into a single dimension, termed task-non-routineness (Daft and Macintosh 1981, Karimi et al. 2004), a concept that we follow in the current paper. Based on previous research studies of management, we view task non-routineness as the level of structuredness, analyzability, difficulty and predictability of a task. Tasks of low non-routineness (high routineness) include the processing of travel expenses or the procurement of standard items, whereas tasks of high non-routineness are typically more difficult to accomplish (e.g., strategic planning, solving of unique problems, and managerial decision-making).

The completion of non-routine tasks are a

common challenge for distributed teams (Majchrzak, Malhotra and John 2005). Task Interdependence Identified as a second dimension of managerial tasks (Goodhue and Thompson 1995, Karimi et al. 2004), task interdependence has been defined as the exchange of output between segments within a

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subunit and with other organizational units (Fry and Slocum 1984). Interdependence requires coordination between activities (Malone and Crowston 1994) and, thus, lends itself well to technological support. Research on task interdependence dates back to Thompson (1967) who was concerned with different mechanisms to achieve organizational coordination. Thompson (1967) proposed that different types of interdependence (e.g., pooled, sequential, reciprocal) existed that required different coordination mechanisms (e.g., standardization, plan, and mutual adjustment) depending on the technologies applied in an organization. For example, when interdependence increased from pooled to sequential to reciprocal, coordination mechanisms should change from rules to standardization to mutual adjustment, as the later required a greater amount of communication as a means for coordination (Thompson 1967). Thompson’s (1967) three interdependency types are thought to contain “increasing degrees of contingency, coordination difficulty, and cost” (Barki and Pinsonneault 2005). We include task interdependence into our analysis as the degree to which a task is related to other tasks and organizational units, and as a result the extent to which coordination with other organizational units is required (Thompson 1967). The level of interdependence determines the user’s need to obtain access to an IS to perform a task as part of a larger whole, which has a direct impact on users’ performance and an indirect impact on the performance of others (Gebauer and Shaw 2004).

Highly

interdependent tasks, such as the development of an advertising campaign, require process actors to interact extensively to generate the desired outcome, while a task having no interdependence, such as telemarketing, can be executed entirely by one person (Wageman and Gordon 2005). Interdependence can be operationalized with the number of regular communication channels and partners that a user interacts with, the pattern of interaction between tasks and resources that are consumed and produced jointly or individually (Crowston 2003), or the level of connectedness (e.g., Gantt-diagram). Time Criticality Time criticality, defined as the importance with which a task needs to be performed promptly (urgency) depicts the dynamics of managers’ work environments and tasks. Even though time criticality has generally received limited attention by scholars of organization science, the ability of organizations to

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respond quickly to changing market requirements has been discussed in management and strategy research (D’Aveni 1994, Bradley and Nolan 1998). For example, Straub and Karahanna (1998) found that the urgency (time-criticality) of communication tasks influence communicators’ preferred type of media (synchronous vs. asynchronous) selected to support the task. The concept of time-criticality has also captured the attention of scholars of mobile IS. Junglas and Watson (2003) described time-dependency as relevant to mobile commerce, while Balasubramaniam, Peterson and Jarvenpaa (2001) mentioned time criticality as an important dimension of mobile systems. Liang and Wei (2004) suggested that mobile commerce was well suited for emergency and time-critical services (similar: Yuan and Zhang 2003), while Siau, Lim and Shen (2001) stated that mobile technologies provide immediacy. Jarvenpaa, Lang, Takeda, and Tuunainen (2003) found that users’ value of mobile devices and services revealed their desire to obtain rapid feedback. Venkatesh, Ramesh and Massey (2003) concluded that time-criticality as a trigger for use might be more important in wireless than in wired environments, which could explain why time-criticality has not found more consideration in organization literature. In practice, support for urgent tasks (e.g., notification of emergency situations) has been among the earliest applications of mobile technologies (Ammenwerth, Buchauer, Bludau and Haux 2000). Mobile IS Research on mobile IS has evolved in recent years. Scholars have provided conceptual overviews of the industry value chain (Barnes 2002), identified development and research issues (Tarasewich, Nickerson and Warkentin 2002, Varshney, Malloy, Jain and Ahluwalia. 2002, Varshney and Vetter 2001), conceptualized business models for telecommunication services providers, devices and applications (Haaker et al. 2004, Varshney and Vetter 2001), identified strategies for system development (Kemper and Wolf 2003, Krogstie et al. 2004), and detailed development cost and infrastructure standards (Balasubramaniam et al. 2001). To develop a profile of TTF for mobile IS, we focus on studies that emphasize the user perspective, and characterize mobile IS along three dimensions: functionality, user interface, and adaptability (similar: Siau and Shen 2003). Functionality is conceptualized to be applicable

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to both mobile and non-mobile IS, whereas user interface and adaptability are conceptualized with a particular focus on mobile IS. Functionality The use of functionality to characterize technology is in line with earlier applications of the theory of TTF, as Goodhue and Thompson (1995) used functionality as one proxy for their technology construct, while Cooper and Zmud (1990) recognized the functional differences between two systems. Dishaw and Strong (1998, 1999) applied a functional view of technology stating that “software will be used if the functions available to the user support the activities of the user (1998, p. 109) ”. Zigurs and Buckland (1998) used functionality to define GSS technology “as a set of communication, structuring, and information processing tools that are designed to work together to support the accomplishment of group tasks.” Malhotra and Majchrzak (2004) identify four different types of support provided by information technologies for distributed groups: task coordination, external connectivity, distributed cognition, and interactivity. We view functionality to refer to the capabilities of the mobile IS. Based on the notion that mobile IS combine traditional computing functionality with interpersonal communication functionality (Balasubramaniam et al. 2001, Krogstie et al. 2004, Sarker and Wells 2003, Varshney et al. 2002, Yuan and Zhang 2003), we categorize mobile IS functionality according to two dimensions, namely (1) whether the main focus is on interpersonal interaction or on computing, and (2) whether the direction of the interaction between the user and the system is one-way or two-way interactive (reciprocal) (Balasubramaniam et al. 2001). The resulting classification scheme includes four functionalities (Gebauer and Shaw 2004, similar: Yuan and Zhang 2003), as described and exemplified in Table 1. User interface of mobile IS As a second factor, the user interface includes a set of features that together describe the experience to use an IS (“look & feel”). While functionality as a characteristic of IS can generally apply to both mobile and non-mobile technologies, the user interface is more of an idiosyncratic factor, insofar

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as mobile technologies bring attention back to the devices used to access and utilize functionality. No longer can we assume that a stationary computer with a standard monitor and keyboard is used. While mobile devices, such as cellular phones, personal digital assistants, laptops, pocket and tablet PCs, and one- or two-way pagers, can all be considered more portable than their stationary counterparts, considerable differences exist in terms of form factors (e.g., size, weight, output and input devices) and other system elements (e.g., processor and battery performance, storage capacity, bandwidth requirements, menu structures, and user dialogue). Developments are ongoing and new devices and applications reach the market constantly (Computerworld 2003, Durlacher 1999, Scudder 2002, Yuan and Zhang 2003). Many researchers have stressed the importance of the user interface for the design of mobile IS (Balasubramaniam et al. 2001, Barnes 2003, Chan, Fang, Brzezinski, Zhou, Xu, and Lam 2002, Krogstie et al. 2004, Lee and Benbasat 2004, Siau et al. 2001, Siau and Shen 2003, Smith et al. 2002, Tarasewich 2003b, Tarasewich et al. 2002, Varshney et al. 2002, Varshney and Vetter 2001, Yuan and Zhang 2003). Acknowledging the complexity and breadth of the user interface, we will exemplify the requirements for the user interface as related to individual use situations and include menu-structures, user dialogue and help features, setup requirements, bandwidth requirements, and system performance into the analysis. The user interface of mobile IS is both a limiting and an enabling factor that requires careful management (AlHawamdeh 2004), and additional research on the requirements and impacts of the user interface of mobile IS is needed. Adaptability The possibility to adapt mobile IS to various use situations is a differentiator and a key enabler for mobile commerce business models (Balasubramaniam et al. 2001, Kini and Thanarithiporn 2004, Lee and Benbasat 2004, Yuan and Zhang 2003). Two forms of adaptation can be distinguished: an adaptation of the application to the physical location of use (i.e., location-awareness) and an adaptation of the application to the individual user and use situation (i.e., personalization). Rao and Minakakis (2003) discussed requirements and business models for location-based services, and examples of location-aware

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mobile IS have been provided in Computerworld (2003). Junglas and Watson (2003) identified two characteristics of tasks that are relevant with respect to adaptability: (1) location-dependency (i.e., situations in which the users’ location is important), and (2) identity-dependency (i.e., situations in which the users’ identity matters).

We also suggest the inclusion of features that allow for consistency

throughout changing use conditions, such as when a user is moving between locations and as a result may experience service disruptions. In the following, we view adaptability as the ability of a mobile IS to adapt to varying circumstances, such as use locations (location-based services), use situations (disruption management), and users (personalization). A device with high adaptability would provide services specific to the user and its location, while devices with little or no adaptability would not. Mobile Use Context Ubiquity, allowing for the reaching of users anywhere and anytime as well as providing anywhere and anytime access to information resources, has been identified as a defining factor of mobile IS (Junglas and Watson 2003, Kemper and Wolf 2002, Kini and Thanarithiporn 2004, Siau et al. 2001, Tarasewich et al. 2002, Varshney et al. 2002, Yuan and Zhang 2003). The desirable situation of ubiquity, however, is often limited in several ways (Gebauer and Shaw 2004). To account for limitations, it has been suggested to include the individual use-context into the design of mobile IS (Siau et al. 2001), acknowledging that generally, the use-context in a mobile environment tends to be less stable than a home or office environment (Tarasewich 2003a). Building on Chan et al.’s (2002) characterization of usability, we focus on user distraction, network connection quality, and user mobility. Distraction Distraction has been mentioned as a factor characterizing the use-situations of mobile IS. Lee and Benbasat (2004) found that users tended to multi-task when using mobile commerce applications, while Tarasewich (2003a) stated that mobile users tended to have more distractions competing for their attention. Chan et al. (2002) found that users had limited time and cognitive resources to spare for performing tasks in mobile environments. Tarasewich (2003a) pointed out that mobility can lead to

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frequently changing use-contexts and safety issues (e.g., driving) limiting users attention (Tarasewich 2003a). In the following, we conceptulize distraction as the level of interference with the use of the mobile system caused by activities and people in the use environment. Consequently, a high level of interference caused by a large number of activities or people demanding a user’s attention cause a high level of distraction, while a low number of activities or people suggests a low level of distraction. We also consider a user to be distracted in a case where outside noise (e.g., a construction site) prevents the user from picking up the signal of an incoming call or message. Connection Quality Network connectivity has been identified as a critical issue to the success of mobile IS and mobile commerce. Varshney and Vetter (2001) identified network reliability as a technical requirement for mobile applications to work properly. Chan et al. (2002) and Siau et al. (2001) suggested that wireless network connections typically provide less bandwidth and tend to be less stable and predictable than wired network connections. Kini and Thanarithiporn (2004) found access speed and availability drive the adoption of mobile commerce, while Balasubramaniam et al. (2001) and Varshney et al. (2002) found that network coverage and reliability impacted the usefulness and feasibility of mobile IS. Kim and Steinfield (2004) found that connection quality impact user satisfaction and continued intention to use mobile services. In our study, connection quality includes factors such as network availability, bandwidth, and stability. High connection quality suggests that usage is not limited by network connections, while low connection quality suggests that usage is affected by network connections. Mobility Mobile IS requires the mobility of at least one participating party (Balasubramaniam et al. 2001). Krogstie et al. (2004) identified several types of mobility, including spatial, temporal and contextual mobility. Sarker and Wells (2003) used the modality of mobility (type and extent) to assess mobile services adoption and use. We define mobility as the extent to which a mobile IS is being used at different geographic locations or while the user is in motion. Mobility refers to location changes over long

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distances (e.g., by car, train or airplane) and short distances (e.g., moving from an outside construction site to an inside office). A high level of mobility suggests that the user changes locations frequently and that the locations are very different from each other (e.g., noisy vs. quiet environments, widely varying access requirements across different countries), while a low level of mobility suggests that the user rarely changes locations during use and that the locations of use are very similar to each other. We consider mobility to be particularly relevant as it is related to the other use variables of distraction and network connection quality. As a user moves between locations, the availability and quality of the network connection can become a usage issue (Chan et al. 2002, Tarasewich et al. 2002) and a user’s level of distraction can be impacted. Fit Researchers have used a variety of measures to assess the fit between tasks and technologies. Goodhue and Thompson (1995) identified eight dimensions of fit as perceived by the users, including measures for the quality and accessibility of data, ease of system use, system reliability, and the relationship between the IS group and system users. The model measured separately the influences of task and technology on fit. Dishaw and Strong (1999) computed the level of fit by matching available functionality with the functionality required and/or anticipated by users to complete various tasks. Zigurs and Buckland (1998) viewed fit as viable alignments (i.e., ideal profiles) of task and technology and confirmed TTF by testing the performance effects of the task-technology alignments (Zigurs et al. 1999). Junglas and Watson (2003) used a pre-determined profile of TTF to the characteristics of mobile IS (ubiquity and uniqueness) and task characteristics (dependency on time, location, and identity). Results showed an impact of TTF on technology use and an impact of use on performance. To assess the commercial and managerial viability of mobile commerce applications, Liang and Wei (2004) presented a fit-viability framework where TTF was roughly the match between task requirements and three attributes of mobile commerce applications: location-sensitivity, time-criticality, and personalization, whereas Gebauer and Shaw (2004) conceptualized fit as user-perceived usefulness in a study of success factors and impacts of a mobile e-procurement system.

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Similar to Zigurs and Buckland (1998), we apply a systematic approach to determine TTF as a predefined profile, assuming that a good fit between tasks and technology has a positive impact on performance and on technology success. As with Zigurs and Buckland (1998), we view performance generically as the ability to reach stated task goals, to be operationalized in greater detail for individual use situations. While Zigurs and Buckland’s (1998) TTF profile included the two independent constructs of group tasks and GSS, we consider three independent constructs, and use a three-step process to determine fit between managerial tasks and mobile IS, moderated by the mobile use context (Figure 1).

In the first step of analysis, we conceptualize the fit between managerial tasks and the IS independent of the use context, i.e., in principle applicable to a mobile and non-mobile IS (Fit 1). Fit 1 is the construct most similar to the various concepts of TTF developed elsewhere. In the second step of analysis, we focus on the features of a mobile IS to determine its feasibility in a mobile use context (Fit 2). The second step is performed largely independent of a particular task. In the third and final step, we join Fit 1 and Fit 2 to derive task-technology fit for mobile IS in support of managerial tasks (Fit 3). One of the results of the analysis is the insight that the greater the differences between the mobile use context and an ideal (non-mobile) use context, the more limited the support provided by a mobile IS for a given task, and the more demanding the requirements of the IS (e.g., the user interface). Fit 1: Managerial tasks and IS To conceptualize fit between a managerial task and an IS independent of the (mobile) use context, we abstract from the technology features related specifically to mobility. We, thus, focus on system functionality only and omit the dimensions of the user interface of the mobile system and of adaptability. Task Non-Routineness: Media-richness theory provides a good basis to derive requirements for the ideal support of managerial tasks depending on the level of task non-routineness. Introduced by Daft and Lengel (1984) the concept of media richness links managerial tasks with different types of information and communication technology best suited to provide support. Daft and Lengel (1984) described the range of managerial tasks from simple to complex and proposed that rich media (e.g., the

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telephone and face-to-face meetings) were needed to process complex tasks (e.g., setting organizational goals, strategies, communicate managerial intentions, and manage employee motivation), while media low in information richness (e.g., written information sources, technical manuals and mathematical formulas) were best to handle simple tasks (e.g., inventory control). Applications of Daft and Lengel’s (1984) media richness theory to information and communication systems have largely confirmed the theory. Leonard, Brands, Edmonson, and Fenwick (1998) found that virtual development team members generally preferred and used richer media for more complex tasks. Lim and Benbasat (2000) found that task analyzability (Perrow 1967) influenced the type (richness) of information representation that was most appropriate for equivocality reduction (Daft and Lengel 1984) and perceived usefulness of an IS (e.g., rich representations, rather than less rich representations, helped decision makers cope with less analyzable tasks). Malhotra and Majchrzak (2004) and Majchrazk, Rice, King, Malhotra and Ba (2000) argue that the level of routineness (non-routineness) of a task determines the technology support required by the task. We view IS that allow for interactive communication and flexible information access as information rich, whereas systems that predominantly structure and automate data processing are perceived as information poor. Consequently, we propose the following: Proposition 1a: Managerial tasks of low non-routineness (i.e., high routineness) should result in best performance when using an IS that emphasizes data processing. Proposition 1b: Managerial tasks of high non-routineness should result in best performance when using an IS that emphasizes communication and information access. Task-Interdependence: Straus and McGrath (1994) and Andres and Zmud (2002) found that highly interdependent tasks required richer information exchanges to clarify task assignments and project requirements, develop effective task performance strategies, make decisions, and obtain performance feedback. These results are in line with Thompson’s (1967) suggestion that the higher the level of interdependence, the more difficult and less standardized the suggested form of coordination (see also Daft and Lengel 1984).

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We conclude that communication is better suited than structured data processing to support situations of high interdependence given the higher degree of media-richness (information access is somewhat in the middle). We also propose that notification is well suited to support situations of high interdependence, as this functionality can help alert team members of waiting tasks and prompt their completion. While situations of high task interdependence lead to added coordination requirements and presumably complicate IS-based support, situations of low task interdependence generally do not result in added coordination requirements. Thus, we focus on the case of high interdependence and propose the following: Proposition 2: Managerial tasks of high interdependence should result in best performance when using an IS that emphasizes notification and communication. Time Criticality: To support time-critical tasks, notification applications (e.g., the use of numeric pagers for emergency alerts) were among the earliest applications of mobile IS. Gebauer and Shaw (2004) found that notification helps management users cope with immediacy requirements. We propose that in cases where a task needs to be performed promptly, notification of the team members about the waiting task is particularly critical and useful. As the requirements to support non time-critical tasks tend to be less specific than the situation of highly time-critical tasks, we focus on timecritical tasks. Proposition 3: Managerial tasks that are highly time-critical should result in best performance when using an IS that emphasizes notification. Table 2 summarizes the proposed match between managerial task characteristics and IS functionality. The cells in Table 2 that are marked with “X” indicate the IS functionality that fit best with the task-characteristics as outlined above, and the corresponding propositions are indicated in parentheses. The logic of Fit 1 is such that each individual task characteristic (non-routineness, interdependence and time criticality) relates to Proposition 1a or 1b, plus possibly also to Proposition 2 and to Proposition 3, with each proposition stating the basic requirements of the task with respect to IS functionality.

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Since every task has some level of non-routineness, a focus on particular functionality is recommended according to Propositions 1a and 1b. In cases where a task is in addition highly interdependent with other tasks, Proposition 2 is applied to recommend the provisioning of notification and communication functionality to enable coordination. Furthermore, in cases where high time-criticality applies, Proposition 3 is applied recommending the provisioning of notification functionality. Further research is required to determine the relative strengths of the impacts of the three propositions on task performance. Fit 2: Mobile IS and mobile use context We now focus on the impacts of mobile use context dimensions on the feasibility of mobile IS. In addition to functionality, we now include the features specific to mobile IS, i.e., user interface and adaptability. The underlying assumption is that compared to the “ideal” work situation presumably found in traditional, stationary office environments, the feasibility of mobile IS is limited inasmuch as the mobile use context is characterized by high user distraction and mobility, and a low level of network quality. Our analysis is largely based on earlier studies that have acknowledged the relevance of the task context for the development and deployment of mobile IS, and on our own observations. We acknowledge a relative lack of rigorous empirical studies on the feasibility of mobile IS to support various individual use contexts, effectively leaving the validation and verification of each proposition a research challenge on its own, together comprising a full research agenda. Level of distraction high: Studies support the notion that situations of distraction require careful consideration of the design of mobile applications in terms of functionality and user interface. Lim and Benbasat (2004) stated that mobile settings limit user attention and pose specific interface design requirements (e.g., form factors). Chan et al. (2002) found that inappropriate mobile IS design caused information overloading as too much demand was placed on the user’s memory and that transactions could not be too complicated if users were distracted or if connections could break. For tasks that required

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much time for decision making and extensive information exchange (e.g., trip planning), desktopcomputers were the most appropriate platform. We conclude that in addition to a carefully designed user interface (e.g., providing clear visual cues), simple system functionality (notification, communication) can help make up for the limited attention span of a distracted mobile user. In addition, verification features to acknowledge that a sent message has actually been received and viewed by the user should be useful. The following propositions summarize our recommendations for use situations of high distraction. Proposition 4a: A mobile use context generally characterized by high user-distraction should be supported by notification functionality in combination with verification, and by communication functionality for best overall task performance. Proposition 4b: A mobile use context generally characterized by high user-distraction should result in suboptimal task performance when primarily supported by information access functionality and by data processing functionality. Proposition 4c: A mobile use context generally characterized by high user-distraction should be supported by a specially designed user interface, e.g., providing targeted cues and requiring minimal attention, for best overall task performance. Quality of network connection low: The quality of a wireless network connection can be low or even non-existent because of limited coverage, bandwidth, or network instability. Poor network quality is problematic as it is the network connection that allows the user to access the regular corporate information infrastructure. Poor network connections can hinder the usefulness, feasibility and success of mobile IS (Beulen and Streng 2002, Chan et al. 2004, Gebauer and Shaw 2004, Varshney et al. 2002). We propose that of all functionalities, notification combined with verification to the sender as to whether the message has been received by the mobile user is the easiest to provide in cases of limited network connectivity. Providing more complex information access and data processing functionalities as well as communication functionality is more difficult (Chan et al. 2002). The recommendations for the

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user interface in situations of poor connection quality include limitations of required up- and downloadtimes and bandwidth, as well as indicators of connection quality. Proposition 5a: A mobile use context generally characterized by low quality of network connection should be supported by notification functionality in combination with verification for best overall task performance. Proposition 5b: A mobile use context generally characterized by low quality of network connection should result in suboptimal task performance when primarily supported by communication, information access and data processing functionalities. Proposition 5c: A mobile use context generally characterized by low quality of network connection should be supported by applications with a specially designed user interface, e.g., including limited bandwidth requirements and indicators of network quality, for best overall task performance. User Mobility: Situations of mobility present a challenge primarily because they create changes of the level of user distraction, and regarding the availability and stability of network connection (Balasubramaniam et al. 2001, Tarasewich et al. 2002). The adaptability and location-awareness of a mobile application can be particularly helpful to support IS use in varying locations and use situations (Liang and Wei 2004, Rao and Minakakis 2003). Chan et al. (2002) found that users on the move were not always aware when their signal strength was weakening or when it was too low for connections. Similarly, adaptability of an IS could provide for changing ring-mechanisms, depending on whether the user is outside or inside, for an automatic adjustment to local time, and for location-based services, such as help to find a hotel, in addition to allowing others to locate the moving user. Finally, it is recommended that in the case of high mobility, the user interface of a mobile IS should allow for the continuation of tasks that were disrupted due to sudden changes of network quality and user distraction. Proposition 6a: A use context generally characterized by high user-mobility should be supported by a user interface that can accommodate changes in the levels of user distraction and of the quality of network connection for best overall task performance.

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Proposition 6b: A use context generally characterized by high user-mobility should be supported by adaptability (location-awareness and customization) of the mobile IS for best overall task performance. Table 3 summarizes the results of the previous discussion regarding the requirements of mobile IS to support a mobile user. The table highlights that use situations of high user distraction and low quality of network connection are particularly difficult to support, given restrictions on the feasible range of particular functionalities, in addition to requirements of the user interface. Support requirements for situations of high user mobility are primarily defined by the user interface and adaptability without restrictions of the feasible range of functionalities. In comparison, we assume that a mobile use context characterized by low user distraction, high quality of network connection, and low mobility does not lead to requirements on functionality, user interface, and adaptability beyond what is required to support a user in a regular, stationary use context. Fit 3: TTF for Mobile IS Based on the previous discussion, we are now ready to derive TTF for mobile IS to support managerial tasks in mobile use contexts (Fit 3), based on a combination of Fit 1 (Propositions 1 to 3) and Fit 2 (Propositions 4 to 7) and using a procedure of some resemblance with the mathematical procedure of matrix multiplication. The result is depicted in Table 4 where each column represents a summary of one row in Table 2 (i.e., Fit 1 between managerial task and IS functionality) and each row represents a summary of one row in Table 3 (i.e., Fit 2 between mobile IS and mobile use context). Consequently, each cell in Table 4 contains the combined requirements of Fit 1 and of Fit 2, thus summarizing the proposed TTF profile for mobile IS to support managerial tasks in a mobile use context. The ratings of -, 0, and + refer to the level of overlap of system functionality that is required by the task and system functionality that is realistically feasible in a given mobile use context, ranging from no overlap, to some overlap to complete overlap, respectively.

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Discussion We have derived a number of propositions for TTF of mobile IS, based on previous research in management, mobile technology and IS (TTF). As depicted in Table 4, the mobile use context effectively limits the feasibility of certain task-technology combinations. Two issues are highlighted. First, each use condition likely to occur in a mobile use context (high distraction, low quality of network connection, and high user mobility) adds to the requirements of an effective and powerful user interface of mobile IS. Second, the feasibility in the functional scope of mobile IS is limited in particular in use situations of high distraction and of low quality of network connections. As a result, the significance of an effective user interface and of the difficulties to support situations of high distraction and of low quality of network connections becomes even more obvious. In order to derive a full profile of fit requirements for a particular combination of a mobile IS, managerial task, and mobile use context, all cells in Table 4 that are located at the intersection of a column representing an applicable task characteristic and of a row representing an applicable characteristic of the mobile use context have to be included into the analysis. For example, let us assume a manager travels to locations where the preferred mobile carrier does not provide adequate network coverage (low quality of network connection). Let us also assume the manager is expected to receive and approve purchasing requests from her staff, i.e., the task at hand is characterized by high routineness, high interdependence and possibly high time-criticality. According to Table 4, the requirements summarized in the row representing low quality of network connection (row #2) applies, as well as the columns representing high task routineness (column #1), high interdependence (column #3), and high timecriticality (column #4) apply. The content of the resulting three cells can be summarized as follows. •

Fit 3 calls for data processing, communication, and notification to support a task of high routineness (P1a), high interdependence (P2) and high time criticality (P3), yet allows primarily for notification in combination with verification because of low quality of network connection (P5a, P5b).

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•

Additional user interface requirements include measures to account for the limited quality of network connection (e.g., limited bandwidth requirements, indicators of network quality) (P5c). The conceptual model, thus, indicates difficulties with achieving an overall fit for the situation

just described. In particular, the feasibility of the functional requirements seems to be problematic, resulting in considerable requirements of the user interface. In an extreme case, a full set of task requirements pertaining to non-routineness (high, low), high interdependence and high time-criticality has to be combined with the full set of restrictions stemming from the mobile use context, including high distraction, low quality of network connection, and high mobility. The difficulties of such an undertaking are obvious. As argued by Orlikowski, technology use involves a “repeatedly experienced, personally ordered and edited version of the technology artifact, being experienced differently by different individuals and differently by the same individuals depending on the time or circumstance (2000).” Thus, an additional factor that is noteworthy is user experience. Previous mobile IS experience has been mentioned as relevant to system success (Gebauer and Shaw 2004). Beulen and Streng (2002) found that familiarity with mobile applications had an increasing impact on the success (perceived usefulness) of the mobile IS over time. Khalifa and Cheng (2002) found the role of exposure (e.g., trial, communicating with and observing others) on the intention of adopting mobile commerce to be significant. Schwarz et al. (2004) proposed that the compatibility of prior experience with prior expertise would help determine overall compatibility of the technology and perceived ease of use. Despite the current importance of the lack of previous experience with the new technology, we have not included experience as a factor to determine the ideal profile of task-technology fit, based on the assumption that the relevance of (limited) experience will decrease over time when users become more familiar with both mobile devices and applications. Still, we recognize that the support of inexperienced users generally adds to the requirements of usability because limiting form factors of mobile devices tend to make usage more difficult especially for inexperienced users. We, thus, suggest that a carefully designed user interface is even more critical to support a novice user in a mobile environment than it would be in a traditional, wired PC-environment. In

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sum, we emphasize that situations of limited user experience further increase the requirements of a powerful and effective user interface. Even though the model presented in the current paper does not provide concrete guidelines of how to manage the various conflicts and tradeoffs between task, technology and use context characteristics required for optimal task support, the model does provide a valuable basis for more informed decision making. In that sense, we provide more than a checklist of requirements and of stumbling blocks as the model can help identify the limits of IS management and help find alternatives. For example, a decision to equip the manager just described with a mobile electronic procurement system (data processing functionality) provides adequate support for the task at hand (Fit 1), but results in a poor fit with the individual use context (Fit 2). Alternative solutions can be found by changing the individual use context or by changing the task itself. For example, the organization might decide to allow the manager to use a different wireless provider at the location of travel, even if such a provider is not on the list of preferred providers and therefore more expensive. In that case the “condition” of low quality of network connection has been eliminated and a fit between task and technology can in fact be achieved. Similarly, in some cases it may be possible to adapt the task to the individual use context. For example, instead of providing the manager with a complete application to perform data processing (e.g., approval of purchasing request), it might be feasible to merely notify the traveling manager of a waiting task and subsequently provide for the delegation of the task to a staff-member with more favorable use conditions (e.g., better network connection). Contributions and Outlook Building on earlier research studies on TTF (Goodhue and Thompson 1995, Zigurs and Buckland 1998), the current research study is based on the assumption that a good fit between a task and an IS positively impacts task performance, pointing to the importance of a good understanding of the requirements for task-technology fit. We presented a conceptual model to determine the fit of managerial tasks to be supported by mobile IS (Fit 3), by taking into account the requirements of an IS in support of a given managerial task (Fit 1), as well as the feasibility of the suggested system in a given mobile use

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context (Fit 2). The model contributes to the IS research areas of TTF and of mobile IS, as well as to management practice. Contributing to the research area of IS, we effectively extend the applicability of earlier research studies on TTF by taking into consideration the use context as a factor impacting the feasibility of TTF. Similar to the example provided above, Gebauer and Shaw (2004) reported on a global Fortune 100 company that unsuccessfully implemented a mobile electronic procurement system to support various tasks of high routineness. According to the conceptual model presented in the current paper, conditions of “traditional” TTF (Fit 1) were met, given that the mobile electronic procurement system supported a task similar to the task supported by its non-mobile counterpart with comparable functionality, but Fit 1 alone cannot explain the fact that the mobile e-procurement system was not successful. An analysis of the use situation according to the conditions of Fit 2, however, reveals additional insights regarding the system’s feasibility and identifies use context requirements that were not being fulfilled. As a second contribution to the research area of TTF, the three-step procedure to determine TTF presented in the current paper is quite flexible in its basic structure and can be applied to different types of IS and use contexts. For example, instead of analyzing the requirements of managerial tasks, Fit 1 could be replaced by Zigurs and Buckland’s (1998) analysis to determine the fit of GSS to support group tasks. Fit 3 would then describe a suggested fit profile of GSS to support group tasks to be applied in mobile use contexts. Furthermore, the proposed procedure of deriving fit by using an approach similar to procedures of matrix algebra can become a useful tool in other research settings. The proposed approach will be particularly helpful to describe and model complex situations where individual conditions can be expressed in a vector-like form. Researchers of mobile IS, such as Siau and Shen (2003) have found that mobile technologies provide only limited support for complex transactions and that the usage of mobile devices is limited in complicated environments. The analysis conducted in the current paper provides a more sophisticated picture and can thus improve our understanding of the requirements of successful mobile IS. In

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comparison to earlier research contributions on mobile IS, the conceptual model presented is grounded in established IS and management theory, and can therefore help improve the rigor applied in mobile IS research. The model also serves to position mobile IS research with respect to established areas of research in IS and management. The model outlines a comprehensive research framework of mobile IS, with each cell pointing to a specific research agenda. Table 3 particularly highlights the need for further research on the mobile user interface, given that, so far there are few theoretically grounded and empirically validated guidelines of how to design powerful and effective systems in general and of how to meet the requirements for mobile user interfaces in particular. Several guidelines for mobile IS management can be derived from the current analysis. First, the conceptual model uncovers that in general, the less the individual use context of the mobile user resembles a “regular” office work environment with a typically assumed low level of distraction, high level of network connection quality, and low mobility, the more difficult it is to provide adequate IS support. In particular, situations of high distraction and of low quality of network connection often found in mobile use contexts restrict the feasibility of TTF (see also Nicholson, Nicholson, Parboteeah, and Valacich 2005). In both cases, the functionalities required by the task are difficult to provide, and meeting the requirements of the user interface becomes critical. The model can furthermore be used to analyze the success of particular mobile IS and to explain the fact that to date, some of the more successful mobile IS provide simple, yet highly task-oriented functionality such as notification and basic communication (see also Gebauer and Shaw 2004). To this extent, the model can also be helpful to design mobile IS from the point of view of a given mobile use context. Based on a good understanding of the requirements of fit between the characteristics of a task, technology and mobile use context, the model points to a number of options, as fit can effectively be achieved by adjusting any or even all of the three factors. In addition to adjustments of the technology (e.g., more adequate user interface), adjustments of the individual use-context (e.g., by subscribing to a more high-quality network provider), and reorganization of the way a task is to be completed (e.g.,

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notification and delegation instead of direct completion), can be taken into consideration, whereby the model provides guidelines to support the management process. The proposed model needs to be tested thoroughly before it can become a practical and applicable tool. To this extent it is necessary to validate the proposed instrument, in particular the dimensions that were used to characterize mobile IS and to describe the mobile use context. So far, most of the dimensions have not been tested empirically, even though they have been stipulated by a number of scholars of mobile IS. In addition, we found little direct support for our quest to match mobile technologies with user tasks and with a mobile use context. While media richness theory provided a good framework to address the first point, we relied on conceptual work and own observations to address the second point. All of the propositions need to be tested rigorously in the future. Furthermore, we suggest taking a critical look at the implications of the proposed TTF on the success of mobile IS, including utilization and organizational performance. In this regard, an analysis of the costs and benefits in relation with the achievement of TTF should be performed to determine organizational value, which can also hold a key to the overall success of mobile IS. Finally, the TTF of mobile IS could become part of a broader analysis to include the actual viability of the systems in question, as proposed by Liang and Wei (2004) in the context of mobile commerce, and ultimately lead to mobile IS that not only provide a good fit with managerial tasks but that also promise managerial and financial success. References Agrawal, M., Chari, K., and Sankar, R. 2003. Demystifying wireless technologies: Navigating through the wireless technology maze. Communications of the Association for Information Systems. 12 166182. Ahuja, M., and Carley, K. 1999. Network structure in virtual organizations. Organization Science. 10(6) November-December, 741-757. Al-Hawamdeh, S. 2004. Usability issues and limitations of mobile devices. N.S. Shi ed. Wireless Communication and Mobile Commerce. Hershey: Idea Group Publ., 247-267. Ammenwerth E., Buchauer A., Bludau B., Haux R. 2000. Mobile information and communication tools in the hospital. International Journal of Medical Informatics. 57(1) 21-40. Andres, H.P., and Zmud, R.W. 2001. A contingency approach to software project coordination. Journal of Management Information Systems. 18(3) 41-70. Anthony R.N. 1965. Planning and Control Systems: A Framework for Analysis. Boston: Harvard Business School, Division of Research.

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Managerial Task Non-routineness Interdependence

Fit 1 (P1..P3):

Time-criticality

Managerial task and information systems (functionality only)

(Mobile) Information Systems Functionality

Fit 3:

User interface

Moderated tasktechnology fit

Adaptability

Mobile use context Distraction Network connectivity

Task Performance

Fit 2 (P4..P7): Mobile information systems and mobile use context (= moderating factor)

Mobility

Figure 1 – A moderated task-technology fit for a mobile information system

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Table 1 –Functionalities of mobile IS Primary Focus Interpersonal Interaction Computing Notification – allows users to Information Access – allows users be reached by others without the to access, but not process (i.e., possibility to respond directly. manipulate), data.

•

Use of pagers and cellular phones to send alerts, e.g., to notify medical and technical staff about events or emergencies requiring response Use of cellular phones for asynchronous communication (access to voice mail, text messages, e-mail)

Communication – allows users to reach others and to be reached by others, including the possibility to interact and respond directly. • • Two-Way (Reciprocal)

Direction of Interaction

One-Way

•

Use of cellular synchronous communication Email writing

•

Access to external data such as yellow pages, white pages, stock quotes, news • Access to internal data, such as reports, corporate directory information • Use of notebook computer with wireless modem to access hospital patient data (Ammenwerth et al. 2000) • Use of laptops to access police radio system (Smith et al. 2002) • Use of laptops to provide onsite insurance quotes Data Processing – allows users to access and process (i.e., manipulate) data. •

phone for (voice) • • • • • • •

Use of handheld devices to process purchasing approval requests as part of an eprocurement system (Gebauer and Shaw 2004) Use of handheld devices for courier and delivery services (Applegate et al 1996) Use of handheld devices for retail inventory management (Ewalt 2002) Use of handheld devices and laptop computers for crop analysis (Thomas 2002) Use of laptops and PDAs for utility plant maintenance logs (Imhoff 2002) Use of tablet PCs for restaurant ordering (Ewalt 2002) Use of satellite technologies to support freight expediting (Smith et al. 2002) Use of portable computers and PDAs to support patient medication administration (Andersen et al. 2002)

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Table 2 – Fit 1: Managerial task and IS functionality, independent of use context; good fit indicated by “X” Information system functionality

Notification

Information

Data

access

processing

Communication

Task dimension Non-

low

routineness

high

X (P1a) X (P1b) X (P2)

Interdependence high Time criticality high

X (P3)

Table 3 – Fit 2: Mobile information system and mobile use context Mobile info. system

Functionality Notification

Communication

Distraction high

Feasible, verification useful (P4a)

Feasibility limited (P4a)

Quality of network connection low

Feasible, verification useful (P5a)

Mobile use context

User mobility high

Info access

Data processing

Feasibility difficult (P4b)

Feasibility difficult (P5b)

Feasible

User interface requirements (examples) Targeted cues, minimal attention interface (P4c) Limited bandwidth, quality indicators (P5c) Continuation of disrupted transactions and communication (P6a)

Adaptability requirements (examples)

Locationawareness and customization (P6b)

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Table 4 – Fit 3: TTF for Mobile IS (derived by combining Fit 1 and Fit 2 with a procedure similar to matrix multiplication) Managerial tasks and info. systems (“ideal” fit) (Fit 1) Mobile info. systems and mobile use context (Fit 2) Level of distraction high • Allows for notification plus verification and communication (P4a) • Limits feasibility of information access and data processing (P4b) • User interface requirements, e.g., targeted cues, minimal attention user interface (P4c) Quality of network connection low • Allows for notification plus verification (P5a) • Limits feasibility of communication, info access, and data processing (P5b) • User interface requirements, e.g., limited bandwidth requirements, indicators of network quality (P5c) User mobility high • Allows for all functionalities • User interface requirements, e.g., to accommodate interrupted transactions and communication (P6a) • Adaptability (locationawareness and customization) useful (P6b)

Task nonroutineness high Calls for communication and info access (P1b)

Interdependence high Calls for notification and communication (P2)

0: Fit somewhat difficult to achieve on functional level (recommended focus on communication)

+: Fit feasible on functional level (recommended focus on notification and communication, plus verification)

-: Fit difficult to achieve on functional level

UI requirements, e.g., targeted cues, minimal attention user interface -: Fit difficult to achieve on functional level

UI requirements, e.g., limited bandwidth requirements, indicators of network quality

UI requirements, e.g., limited bandwidth requirements, indicators of network quality

UI requirements, e.g., targeted cues, minimal attention user interface 0: Fit somewhat difficult on functional level (recommended focus on notification plus verification)

Task routineness high Calls for data processing (P1a) -: Fit difficult to achieve on functional level UI requirements, e.g., targeted cues, minimal attention user interface

UI requirements, e.g., limited bandwidth requirements, indicators of network quality +: Fit feasible on functional level

Time criticality high Calls for notification (P3) +: Fit feasible on functional level (recommended focus on notification) UI requirements, e.g., targeted cues, minimal attention user interface

+: Fit feasible on functional level (recommended focus on notification plus verification) UI requirements, e.g., limited bandwidth requirements, indicators of network quality

UI requirements, e.g., to accommodate interrupted transaction and communication Adaptability useful

34