Smartwatches can be defined as a special form of mobile computers in the shape of digital wristwatches equipped with various sensors and wireless interfaces (Zenker / Hobert 2019, study 1; Cecchinato et al.
2018). They arose in the class of wearable computer devices during the last decades and are today the most prominent representatives. They can be delimited from other devices with a similar shape (like fitness tracker) by the characteristic that they are operated by a hardware-independent operating system, which can be extended by installable applications (McGrath et al. 2013). As all wearable computers, they are worn directly on the users’ body and are therefore always available to the users, independently of a specific location or time (Boronowsky et al. 2008; Rhodes 1997). Due to the possibility to provide haptic feedback (e.g., vibrations) at the users’ wrist, smartwatches can demand attention proactively, which is helpful to initiate interactions, e.g., when an important notification is received. Although smartwatches are technically similar to mobile devices, they are usually limited to simple input and output options due to the small form factor (Malu / Findlater 2015). Nevertheless, users can always interact with them since they wear devices on their bodies. In contrast, for using applications on mobile computers like smartphones, e.g., the Kanban-style task management tool Trello (Atlassian 2020), the device needs to get out of the pocket and to be held in a hand, which is not convenient for employees executing manual work but offers more advanced input and output capabilities (Chaparro et al. 2015). Research on wearable computers started more than 50 years ago (Thorp, 1998; Rhodes, 1997). Most research contributions target (1) technical aspects like sensor requirements for activity recognition (Bieber et al. 2013) or expanded input expressivity through mechanical interaction (Xiao et al. 2014) on smartwatches, (2) designing applications in private or business contexts such as a smart-glasses-based learning system (Hobert / Schumann 2017b) or a smartwatch-based system to support employees in collaborative industrial scenarios (Zenker / Hobert 2019, study 1), (3) the added value of
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wearable computers like studies about augmented reality-based information systems (Berkemeier et al.
2019), industrial deployment of wearable computer in the industry (Lukowicz et al. 2007), or the use of wearable and augmented reality technology in industrial maintenance work (Aromaa et al. 2016) as well as (4) usability aspects regarding smartwatch applications like the Wear OS usabilityWatch framework (Zenker / Hobert 2020, study 6). Some companies presented several smartwatch-based products such as MeisterTask (MeisterLabs GmbH 2020), Hipaax TaskWatch (Hipaax LLC 2020), aucobo (aucobo GmbH 2020), and WORKERBASE (WORKERBASE GmbH 2020). However, available products, to the best of our knowledge, have a limited range of functionality, do not provide any scientific background, or are not eligible for scientific studies, since the source code cannot be accessed.
As smartwatches are not widespread in enterprises yet, we investigate influencing factors for adoption.
This term refers to the acceptance, integration, and use of innovative technology like smartwatches in society. It determines the diffusion of technology. There are many theories about technology adoption in IS research (Oliveira / Martins 2011). The most prominent models concerning the corporate level are diffusion of innovation (DOI) (Rogers 1995) and the technology, organization, and environment (TOE) framework (DePietro et al. 1990), which then was extended to TOEI complementing individual factors (Rosli et al. 2012; Hoong / Marthandan 2014). Especially, TOE is used to assess technology adoption in a broad range of fields. For instance, Lippert / Govindarajulu (2006) utilized it for web services, Demertzoglou (2007) for open source databases, Borgman et al. (2013) for cloud computing, and Chiu et al. (2017) for broadband mobile applications. In addition, there are frequently used models like the technology acceptance model (TAM) (Davis et al. 1989), theory of planned behavior (TPB) (Ajzen 1985), the unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al. 2003), but they apply only at the individual level. Underlying inhibitors and enablers can be seen as a dual-factor concept in technology usage (Cenfetelli 2004) and the user perceptions according to information technology innovations have to be measured with an adequate instrument (Moore / Benbasat 1991). The adoption and diffusion of smartwatches in the corporate context remains still largely unexplored. Nevertheless, there are first efforts like determinants for adoption in the medicinal domain (Su / Gururajan 2010), influencing factors and challenges in the industrial sector (Hobert / Schumann 2017a), or student influencing factors (Adapa et al. 2018) of the adoption of wearable computers in the consumer and corporate context. Furthermore, Page (2015) gives a forecast of the adoption of wearable technology.
Regarding smartwatches, there are predominantly studies within the consumer context. Dehghani (2016), for example, analyzed the adoption and diffusion of smart wearable technologies like smartwatches and fitness wristbands by consumers. Cho / Park (2016) examined the diffusion of smartwatches in the Korean consumer market. The role of usefulness and visibility in smartwatch adoption was amplified by Chuah et al. (2016). Kim / Shin (2015) developed an acceptance model for smartwatches. Besides, device characteristics were in focus. Kim (2016) investigated whether screen shapes like round and squared affect the adoption of smartwatches. Hsiao (2017) identified adoption intention while comparing Apple and non-Apple smartwatches. Choi / Kim (2016) survey factors affecting
the intention to use smartwatches as an IT product or a fashion product. However, research on the adoption of smartwatches in the corporate context remains limited.
2.3 Research Design
To analyze smartwatch adoption and to derive the Smartwatch Applicability Framework as outlined in section 2.6, we apply a threefold mixed-methods approach (see Figure 14). First, we design a software-artifact traversing a process inspired by the design science research model of Peffers et al. (2007).
Second, we utilized the software as an instrument to elaborate on enabling and inhibiting factors for smartwatch adoption considering the four categories introduced by the TOEI framework (Rosli et al.
2012; Hoong / Marthandan 2014) based on interviews regarding a field study. Finally, we construct the Smartwatch Applicability Framework as the main contribution of this paper.
According to our design process, the design of the artifact should be grounded in the problem identification phase (step 1). For that, we rely on the results of a series of workshops that we conducted in summer 2017 within industrial production facilities. The main goal of these workshops was to identify application scenarios for wearable computers, which are beneficial for companies and employees.
Besides, we conducted further focus interviews from November 2017 to August 2019 regarding smartwatches in the corporate context with technicians, team leaders, and managers. This includes employees of two large-scale biotechnology companies, several medium-sized companies within industrial production, and a large-scale university IT department with approximately 700 employees at over 40 different locations. A predominantly mentioned application for smartwatches was the domain of support and maintenance (see section 2.4.1), which then became the focus of this work.
Figure 21. Research design with the respective empirical foundation
Based on this scenario, as the identified real-world problem, we derive functional requirements as the objectives of our solution (step 2). For this purpose, we carefully analyze the documentation of the workshop series and interviews. During these studies, the involved employees explained in detail how
Prototype
Evaluation of the artifact in a mixed-method approach composed of a preliminary questionnaire determining employees’ attitude regarding smartwatches and the presented concept, a concluding questionnaire assessing the impact of the use of the
system in practice during the field study and interviews with involved employees extended by domain experts of three support teams of an large-scale university.
Empirical Foundation
in total 46 employees from various departments of two large-scale biotechnology companies (from technicians to management)
Workshops Interviews
in total seven domain experts (technicians, head of IT-support, IT team leader and head of IT) of middle to large scale industrial companies and a large-scale university
five units (Nov. 2017 - Aug. 2019)
seven units (May - July 2017) approx. 120 minutes each approx. 90 minutes each semi-structured interview guideline
Questionnaire I
seven employees (five mobile supporter and two administrative team leader)
in total 22 experienced IT-support employees within IT support departments of a large scale university (from IT supporter to leader of different teams)
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their current workflows are designed and how they believe that smartwatches can assist them during their daily work. Overall, we acquire four requirements that are the basis for the subsequent design cycle.
Following the design process, we create the supportWatch application based on the deduced requirements (step 3). We build our research on an existing software meta-artifact providing a smartwatch-based IS suited for a broad range of use cases (Zenker / Hobert 2019, study 1). With respect to Iivari (2015), we utilize his strategy one to instantiate the general concept of the meta-artifact into a specific solution to adapt it to our specific support context solving the identified problems in practice.
During this process, new functionalities are added to the artifact. By choosing an agile development approach, we can discuss intermediate prototypes regularly, and we involve a team leader of the IT-support department within a large-scale university in the development process to improve the software constantly until it met all requirements. However, the focus of this work is to investigate the adoption of smartwatch-based IS in the corporate context, and we utilize the prototype as a tool to access related influencing factors.
Subsequently, we conduct a demonstration according to the research design in step 4. For this, we demonstrate supportWatch during four live presentations (each approx. 60 minutes) involving volunteering employees of different teams in the IT department of a large-scale university working in support scenarios. This includes 22 employees working in the teams of student IT-infrastructure support, lecture hall technology support, and electronic assessment support. Most of the employees have several years of experience in their support job. Also, the four team leaders attend the presentations.
Building on that, we enter an evaluation phase (step 5). Subsequently to the demonstration sessions, the participants are asked to fill in a quantitative and qualitative questionnaire with 20 questions to evaluate their attitude regarding smartwatches and their rating of our concept. In this way, we have collected 18 reasonably completed questionnaires. After that, we started a field study for two of the scenarios, including tickets and routine maintenance (see section 2.4.1). Five employees were equipped with smartwatches and used the system for over one month in their daily work (two additional team leaders were involved as supervisors using the web-based component at their desktop environment).
To conclude the study, we performed a second round of the questionnaire with the five employees who have used the smartwatches during the field study and conducted qualitative semi-structured interviews with all eight participants, including the three team leaders. The interviews took approx. 45 minutes each, and the participants are asked about their attitude and experience using the smartwatch-based IS during their daily work.
Finally, we analyze the findings of the interviews by coding and categorize enabling and inhibiting factors influencing the adaption of smartwatch-based IS. We systematize the factors according to four categories as proposed by the TOEI framework (Rosli et al. 2012; Hoong / Marthandan 2014). Finally, we used the identified categories as the starting point for deriving the Smartwatch Applicability
Framework. To demonstrate the applicability, simplicity, and usefulness of the framework, we exert it to the three support related scenarios and an additional scenario from industrial production.