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The purpose of this paper is to analyze the determinants of the performance of regional industrial technology development programs among the regional strategic industrial development program that the central government and Daegu metropolitan city jointly promoted between 2004 and 2012. Specifically, in this research, the authors are trying to identify the effects of R&D capabilities and technical development tasks on technological and managerial performance.

The dependent variables of this study are technical and economic performance. Technical performance, product and process innovation, economic performance, sales and export increases were measured using five-point Likert scales. The authors added the contribution of sales through technology development to economic performance. The independent variable is the company’s R&D capability, measured by the number of R&D staff compared to the average total number of employees from 2004 to 2012. The characteristics of the technology development tasks were measured by technical characteristics, market characteristics and collaborative research types. The technological characteristics were measured by seven factors, including technological change, technical difficulty, potential in commercialization, competition between domestic and foreign competitors, difficulty in introducing overseas technology and the technological gap. Market characteristics were largely divided into complexity, dynamics and competitiveness. The types of collaborative research were divided into whether or not there were collaborative research with the participation of large corporations. The control variables are firm size (number of employees) and firm age. Regression analysis was used to analyze the determinants of performance, and a difference analysis was conducted to determine the effect of collaborative research on performance.

The main determinants of the regional industrial technology development program performance are the characteristics of the technology development task rather than the internal R&D capability; moreover, the technical characteristics, complexity of the developed product market and participation of large corporations had significant effects on R&D capability. The R&D capacity of firms in internal R&D capacity had a significant effect only on the improvement of technology development ability. Therefore, R&D capacity, which is the main determinant of technology innovation, did not have a significant effect on the performance of short-term technology development tasks. Technological change, technological difficulty, competition between domestic and foreign competitors and the technological gap had positive effects on performance, excluding sales contributions. In addition, the complexity of the developed product market such as the diversification of demand, competitive product and sales distribution channels had positive influences on the performance of technology development programs, unlike dynamics and competitiveness. In this study, the authors cannot confirm the effect of collaborative research on the performance of the technology development programs, but they confirmed that collaborative research involving large corporations had a positive influence on performance.

The results of the analysis of the determinants of regional industrial technology development programs suggest some implications in the future evaluation of these regional industrial technology development programs. It is necessary to review the application qualification and merit, advance review of the business plans and confirmation, an examination of the research results and performance of the applicants and a review of the technology and market situation of the project. For this, the authors suggest that the written review from the relevant technical experts be submitted to the evaluation committees. Also, when establishing regional industrial development programs, they should be evaluated thoroughly, including detailed information and contents about the technical and market characteristics of the local industry.

This research is one of the first to investigate the achievements of R&D support programs among regional industrial development programs in Korea. The results of this study can substantially contribute to the development and implementation of the R&D support policies of the central and local governments. Furthermore, the findings suggest guidelines for improving the performance of R&D support programs in the future. A theoretical model for enhancing the efficiency of government R&D support programs may be established, and an empirical analysis may be conducted to provide practical and academic implications for further research.

Online labour markets (OLMs) have recently become a widespread phenomenon of digital work. While the implications of OLMs on worker well-being are hotly debated, little empirical research examines the impact of such work on individuals. The highly competitive and fast-paced nature of OLMs compels workers to multitask and to perform intense technology-enabled work, which can potentially enhance technostress. This paper examines the antecedents and well-being consequences of technostress arising from work in OLMs.

The authors draw from person–environment fit theory and job characteristics theory and test a research model of the antecedents and consequences of worker technostress in OLMs. Data were gathered from 366 workers in a popular OLM through a large-scale online survey. Structural equation modelling was used to evaluate the research model.

The findings extend existing research by validating the relationships between specific OLM characteristics and strain. Contrary to previous literature, the results indicate a link between technology complexity and work overload in OLMs. Furthermore, in OLMs, feedback is positively associated with work overload and job insecurity, while strain directly influences workers' negative affective well-being and discontinuous intention.

This study contributes to technostress literature by developing and testing a research model relevant to a new form of work conducted through OLMs. The authors expand the current research on technostress by integrating job characteristics as new antecedents to technostress and demonstrating its impact on different types of subjective well-being and discontinuous intention. In addition, while examining the impact of technostressors on outcomes, the authors consider their impact at the individual level (disaggregated approach) to capture the subtlety involved in understanding technostressors' unique relationships with outcomes.

As empirical insights into when salespeople should integrate information and communication technology (ICT) into their sales tasks are limited, the purpose of this study is to investigate the impact of salespeople’s ICT orientation on role stress by considering the interplay of individual salesperson characteristics and the complexity of the selling environment, differentiating between customer and supplier complexity.

The authors develop an empirical framework based on the Job Demands-Resources model and previous research in the area of technology in sales. They test their hypotheses by means of a survey of 255 business-to-business salespeople which is analyzed using ordinary least squares regressions.

The results of this study show that ICT orientation generally helps salespeople to reduce role ambiguity. However, the benefits salespeople derive from ICT orientation to reduce role conflict depend on an interplay of both their job tenure and the average relationship duration with customers as well as the complexity of the selling environment.

This study contributes to research on the impact of technology use on salespeople by enhancing the understanding of contexts that make ICT valuable for them. In particular, the findings of this study demonstrate that the impact of ICT orientation on salespeople’s role stress depends on an interplay of individual salesperson characteristics, that is, resources, and environmental complexity characteristics, that is, demands.

Information Systems research on emotions in relation to using technology largely holds essentialist assumptions about emotions, focuses on negative emotions and treats technology as a token or as a black box, which hinders an in-depth understanding of distinctions in the emotional experience of using artificial intelligence (AI) technology in context. This research focuses on understanding employees' emotional experiences of using an AI chatbot as a specific type of AI system that learns from how it is used and is conversational, displaying a social presence to users. The research questions how and why employees experience emotions when using an AI chatbot, and how these emotions impact its use.

An interpretive case study approach and an inductive analysis were adopted for this study. Data were collected through interviews, documents review and observation of use.

The study found that employee appraisals of chatbots were influenced by the form and functional design of the AI chatbot technology and its organisational and social context, resulting in a wider repertoire of appraisals and multiple emotions. In addition to positive and negative emotions, users experienced connection emotions. The findings show that the existence of multiple emotions can encourage continued use of an AI chatbot.

This research extends information systems literature on emotions by focusing on the lived experiences of employees in their actual use of an AI chatbot, while considering its characteristics and its organisational and social context. The findings inform the emerging literature on AI.

The study explores the extent to which business analytics can address business problems using the task-technology fit theory.

The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data.

The study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems.

Extant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors’ knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks.

In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

This paper presents a model aiming to identify the factors influencing the adoption of augmented reality (AR) for industrial services.

The study combines a literature analysis with an empirical study conducted exploring how five large industrial companies are introducing AR for supporting the provision of technical assistance and industrial services to their installed base.

The authors identify four categories (task, workforce, context and technology) that combine 18 factors that manufacturing companies should consider when introducing AR technology to support industrial services.

This paper systematises the fragmented literature on technology adoption and in particular those works related to the factors affecting the adoption of AR in industrial services. Based on literature and empirical evidence, the authors propose a novel framework that can help companies in the selection of AR solution based on their specific applications and situations. This study therefore contributes also to the existing literature on the adoption of I4.0 and digital technologies in industrial services.

Interruptions are prevalent in knowledge work, and their negative consequences have driven research to find ways for interruption management. However, these means almost always leave the responsibility and burden of interruptions with individual knowledge workers. System-level approaches for interruption management, on the other hand, have the potential to reduce the burden on employees. This paper’s objective is to pave way for system-level interruption management by showing that data about factual characteristics of work can be used to identify interrupting situations.

The authors provide a demonstration of using trace data from information and communications technology (ICT)-systems and machine learning to identify interrupting situations. They conduct a “simulation” of automated data collection by asking employees of two companies to provide information concerning situations and interruptions through weekly reports. They obtain information regarding four organizational elements: task, people, technology and structure, and employ classification trees to show that this data can be used to identify situations across which the level of interruptions differs.

The authors show that it is possible to identifying interrupting situations from trace data. During the eight-week observation period in Company A they identified seven and in Company B four different situations each having a different probability of occurrence of interruptions.

The authors extend employee-level interruption management to the system-level by using “task” as a bridging concept. Task is a core concept in both traditional interruption research and Leavitt's 1965 socio-technical model which allows us to connect other organizational elements (people, structure and technology) to interruptions.

This paper aims to provide insights into the potential of digital technologies-based innovations for more inclusive healthcare by alleviating the affordability, accessibility and availability barriers to utilization of healthcare services. Also, it aims to provide insights into the potential of digital technologies-based innovations for more inclusive services, broadly.

A conceptual framework is inductively developed by analyzing real-world examples of digital technologies-based innovations for more inclusive healthcare through the lenses of economics of information in digital form and certain characteristics of services.

Concurrent implementation of digital technologies-based healthcare innovations with innovations and/or modifications in service processes can enable greater inclusivity by alleviating the affordability, accessibility and availability barriers to utilization of healthcare services.

Issues relating to inequities in healthcare, as a social problem, are the focus of research at multiple levels (e.g. global, national, regional and local) in several academic disciplines. In relation to the scope of the problems and challenges pertaining to providing quality healthcare to the unserved and underserved segments of society, worldwide, the contribution of the proposed framework to practice is modest. However, by highlighting the promise and potential of digital technologies-based innovations as solutions for alleviating barriers to affordability, accessibility and availability of healthcare services during various stages (prevention, detection, diagnosis, treatment and post-treatment follow-up) with illustrative vignettes and developing a framework, the article offers insights for future research. For instance, in reference to mission-driven social enterprises that operate in the product-market space for inclusive innovations under resource constraints, a resourcefulness-based view of the social enterprise constitutes a potential avenue for theory development and research.

Given the conceptual nature of the article, the implications for practice are limited to cognitive implications. Action implications (instrumental implications or implications for practice) are outside of the scope of the article.

Innovations that are economically viable, environmentally sustainable and socially impactful is one of the important issues of our times.

The proposed framework provides insights into the potential of digital technologies-based innovations for more inclusive healthcare by alleviating the affordability, accessibility and availability barriers in the context of emerging and less developed country markets and base of the pyramid segments of society in these markets.