Use Contexts of Personal Computing Devices

Use Contexts of Personal Computing Devices: Determinants of Use Context Changes

Jimin Kim, Goya Choi, Younghoon Chang, Myeong-Cheol Park

Abstract This study investigates the use contexts of personal computing devices in multiple steps and conducts in-depth analysis for the use context of smartphones. The determinants of use context changes of smartphones are investigated using the technology-to-performance chain model. In steps 1 and 2, a diary study method and 2014–2015 Korea media panel research data provided by the Korea Information Society Development Institute are used. Correspondence analysis, chi-square independence tests, and standardized residual analyses were conducted. In step 3, this study develops and validates a framework for use context changes using a survey method and structural equation modeling. The results show that the use context of personal computing devices is represented differently and is clearly defined depending on the device used. Furthermore, the use context of smartphones has changed significantly because of the rapid growth of smartphone users and diverse usage patterns of smartphones. The research model results show that users expand the scope and frequency of smartphone use when they experience improved performance in everyday tasks and feel that smartphone content and functions could support everyday tasks better. This study presents novel early-stage research and presents empirical evidence and propositions in both exploratory and confirmatory ways. The main contribution of this study is to provide guidelines and general implications for other empirical studies on the use contexts of devices or IT services.

Keywords Use Context, Computing Devices, Smartphones, Diary Study Method, Technology-to-Performance Chain

Citation: Kim, J., Choi, G., Chang, Y., & Park, M. C. (2017). Use contexts of personal computing devices: determinants of use context changes. Industrial Management & Data Systems, 117(10), 2431-2451.

1.

Introduction

In recent years, information technology (IT) rapidly and dramatically has become a ubiquitous, pervasive part of everyday life (Bødker et al., 2014). Among various changes, increasing diversity of pervasive computing devices and applications will have an increasingly important impact on users (Chaari et al., 2007). Such devices and applications need contextual information, including the type and state of network connection, users’ location, and hours of use. Users of computing devices and IT services may exploit different aspects of devices or services and access different information in different context situations (Chaari et al., 2007). For these reasons, there is a great need for studies and investigations on computing use context. Substantial research has already been undertaken on how to use context data in the development of systems and applications (Chaari et al., 2007). However, context investigations in terms of users are insufficient and difficult to implement. One of the major purposes of this study is to provide guidelines and general implications related to use context studies from users’ perspective. In this study, the use contexts of personal computing devices, changes in use contexts, and determinants of use context changes are investigated. First, the use contexts of personal computing devices are investigated using a diary study method with a large number of samples. This analysis provides understanding for not only computing use context but also multi-device usage behavior. A recent emerging phenomenon is multi-device ownership (Ivaturi & Chua, 2015). Many people employ multiple computing devices simultaneously and display clear multi-device usages behavior (Ivaturi & Chua, 2015). However, the environment of multi-device use has not been explored clearly because it is an emerging phenomenon at this point. In reality, users’ context affects the use of computing devices. For example, users who have desktops or laptops in their rooms or offices (i.e., multi-device owners) sometimes search for news articles, shop online, or use social networking sites (SNSs) on their smartphones. In spite of the small screen size of smartphones, users often perform these activities, and contextual interpretations can be carried out to investigate this behavior. Furthermore, much research that is related to multi-devices is still ongoing, but most studies have been conducted via a series of one-on-one comparisons. In this study, five computing devices (i.e., desktops, laptops, netbooks, tablet PCs, and smartphones) are investigated to derive the relationships between use context factors and computing device use. In the second stage, changes in use contexts are investigated for more sophisticated interpretations. The usage patterns of computing devices, especially tablet PCs and smartphones, are changing rapidly because of the rapid growth in 4G long-term-evolution (LTE) subscribers and the increased number of mobile applications. Through chronological changes in the use context, this research provides a better understanding of usage patterns and environments. Furthermore, an in-depth analysis is conducted into the use context of smartphones because such context differs compared to other devices in any year. Based on the results of previous stages, the determinants of use context changes of smartphones are investigated. This study focuses on users’ tasks in everyday life and the fit of tasks to smartphones. The technology-toperformance chain model, which highlights the influencing relationships in task–technology fit (TTF), utilization, and performance impact, is adopted to explain the determinants of use context changes. The results of this study are expected to provide general guidelines for other empirical studies on the use contexts of devices or IT services.

2.

Literature Review

2.1

Use Contexts in Personal Computing Devices

Use contexts, which are also mentioned in several works of literature as “context in use” or “context of use,” are an increasingly very important (Maguire, 2001; Lee et al., 2005). This concept is highlighted as a key issue in human–computer interaction (HCI) and information systems (Schmidt et al., 1999; Lee et al., 2005). Use contexts refer to the personal and environmental factors that may influence a user when he or she uses a technology or service Kim et al., 2002; Lee et al., 2005). This definition highlights a characteristic of use context, that is, the use contexts are contextual information from the user’s perspective (Lee et al., 2005). Several studies have suggested various use context factors. Kim et al. (2002) and Kim et al. (2005) suggested that the use contexts of mobile internet consist of movement as a spatial factor, behavioral goal, co-location, interaction with other people, visual and auditory distraction, and so on. Lee et al. (2005) suggested that the use contexts of mobile internet use consist of movement, physical context, time, social context, and emotion. Colombo & Scipioni (2014) stated that the use contexts of e-book services on tablet PCs are delineated into spatial context (e.g., location) and temporal context (e.g., time). Müller et al. (2012) suggested that the use contexts factors of tablet PCs are

location of use, place, situation, and activity. Maguire (2001) stated that physical environment, tasks, user goals, user characteristics, social and organizational environment, and technical environment are among use context factors. De Reuver & Bouwman (2010) considered physical environmental factors, tasks, and social environmental factors as the use contexts of mobile internet. In addition, De Reuver et al. (2013) suggested passing time, task-related factors, and social and work factors as use contexts. Furthermore, Arhippainen & Tähti (2003) proposed place, time, and accompanying people as use contexts of mobile application use. According to Perera et al. (2014), users and computing can be understood along with other context elements, such as time, social, networking, and things (i.e., objects). In this research, the following use context factors, which are repeatedly highlighted in previous works, are analyzed in personal computing device uses: time zone, day of the week, weekdays/weekend, use location, movement, activity, network connectivity (see Table 1).

Table 1. Use context factors for the analyses Use Context Factors Time zone Day of the week Weekdays/weekend Use location Movement Activity Network connectivity

References Arhippainen & Tähti (2003); De Reuver et al. (2013); Colombo & Scipioni (2014) Lee et al. (2005); Zimmermann et al. (2007); Kim (2012) Kim et al. (2005); Lee et al. (2005); Kim (2012); Müller et al. (2012); Liu et al. (2014) Kim et al. (2002); Kim et al. (2005); Lee et al. (2005); Kim, (2012) Zimmermann et al. (2007); Müller et al. (2012); Liu et al. (2014) Schmidt et al. (1999); Zimmermann et al. (2007); Maguire (2001)

Few studies have been conducted regarding context changes. Despite these limitations, several studies have important implications. Verplanken et al. (2008) suggested that context change can enhance and guide certain behavior. In addition, context change can lead people to make new choices and decisions because it makes behavior-relevant information more influential (Verplanken et al., 2008). Furthermore, Khabou et al. (2014) presented the importance of analyzing and detecting context changes in a smart campus environment in which various devices are used.

2.2

Technology-to-Performance Chain

The technology-to-performance chain suggested by Goodhue & Thompson (1995) is a model that explains that technologies lead to an individual’s performance impacts. This model provides a sophisticated picture of the way in which technology, user tasks, and utilization are related to changed performance (Goodhue & Thompson, 1995). In this model, technology is a tool used by individuals to accomplish their tasks (Goodhue & Thompson, 1995). The technology can be any computer system (hardware or software) or IT service (Goodhue & Thompson, 1995). The task–technology fit (TTF) refers to “the degree to which a technology assists an individual in carrying out his or her tasks” (Yen et al., 2010). The TTF highlights the concept of fit between technology and tasks (Wu et al., 2011). Furthermore, users might be willing to utilize the technology if it fits with their tasks well (Wu et al., 2011). The technology-to-performance chain is a combined model of two theories. Technically, this model can be divided into two streams: (1) TTF focus (theories of fit) and (2) utilization focus (theories of attitudes and behavior) (Goodhue & Thompson, 1995; Yen et al., 2010). The TTF is mainly affected by task and technology characteristics (Goodhue & Thompson, 1995). On the other hand, utilization is mainly affected by other beliefs in the user’s perception level (Goodhue & Thompson, 1995). One important aspect of the technology-to-performance model is “feedback” from the performance impact. After technology utilization and an experience of performance effects, inevitable feedbacks emerge (Goodhue & Thompson, 1995). Users anticipate that the technology will change the consequences of utilization and these perceptions affect users’ future behavior (Goodhue & Thompson, 1995). Some major studies are related to the technology-to-performance model and TTF. Zhou et al. (2010) showed TTF effects for mobile banking. Yen et al. (2010) showed TTF effects on users’ intention to adopt wireless technology. D’Ambra & Wilson (2004) applied the technology-to-performance model in the use of the World Wide Web. Zigurs & Buckland (1998) tested TTF with the effectiveness of group support systems. McGill et al. (2011) tested the technology-to-performance model in the use of learning management systems. Finally, Larsen et al. (2009) applied TTF and utilization to e-learning.

In this study, the determinants of use context changes of smartphones are investigated based on the technologyto-performance chain because there is a logical link between the use context changes and the performance effects of smartphone usage. Furthermore, the technology-to-performance model can provide more comprehensive understanding for users’ perceptional flow in the wireless technology context (Wu et al., 2011).

3. Analysis 1: Exploratory Findings for Use Contexts of Personal Computing Devices 3.1

Diary Study Method and Data Collection

In analyses 1 and 2, a diary study method was used for collecting data. The diary study method is a self-monitoring method by users to collect users’ contextual information in non-manipulated situations and has been suggested as an effective way to minimize the effects of observers on users (Carter & Mankoff, 2005; Kim, 2012). In Müller et al. (2012), the diary study method is used to understand the context of tablet use. In analysis 1, this study used Korea media panel research data for 2015, provided by the Korea Information Society Development Institute. The data were collected from May 2014 to July 2014 in South Korea. A total of 10,172 participants created diaries for 3 days (total number of samples: 30,516). The participants recorded their usage of personal computing devices every 15 minutes (a total of 96 time slots per day: 15 minute-units during 24 hours). When they used personal computing devices (desktops, laptops, netbooks, tablets, and smartphones), they selected their location, activity, and network connection within a given set of choices. If participants did not use any device, they could select “not used.” After creating the diary, the error values were removed through interviews. The demographic information of each service user is shown as follows.

Table 2. 2014 Demographic information of participants 2014 Demographic Information Gender

Age

3.2

Male Female