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This study aims to investigate the relationship between information and communication technologies (ICTs) use and work performance during the coronavirus disease 2019 (COVID-19) pandemic. Specifically, it aims to understand what the role of task–technology fit is, and what effect this has on feelings of loneliness of individuals and their subsequent work performance. As a large proportion of workers are required to work from home during the ongoing COVID-19 pandemic, understanding what aspects contribute to higher performance and reduced negative psychological outcomes is of increased practical and research interest.

To explore these questions, a quantitative approach that employed a sample population of 357 individuals who worked from home during the COVID-19 pandemic was used. Using a convenience, purposive and snowball sampling approach the authors collected data through a custom-built online questionnaire, and analyzed the data using structural equation modeling (SEM).

The results highlight the effect that high task–technology fit has in both directly and indirectly influencing work performance of individuals. The authors find that by designing ICTs based on task–technology fit principles, individuals are less likely to experience feelings of loneliness while working from home and are more prone to perform better in their work-related tasks.

This study offers a new perspective on the role fit of tasks with technology have on influencing psychological states, and indirectly influencing work-related outcomes. The authors, therefore, expand the understanding about why task–technology fit is sought after by explaining part of the psychological mechanisms through which it has an effect on work performance.

In the complex ecosystem of mobile applications multiple factors have been used to explain users’ behavior, without though focusing on how different combinations of variables may affect user behavior. The purpose of this paper is to show how price value, game content quality, positive and negative emotions, gender and gameplay time interact with each other to predict high intention to download mobile games.

Building on complexity theory, the authors present a conceptual model followed by research propositions. The propositions are empirically validated through configurational analysis, employing fuzzy-set qualitative comparative analysis (fsQCA) on 531 active users of mobile games.

Findings identify ten solutions that explain high intention to download mobile games. Alternative paths are identified depending on the gender and the time users spend playing mobiles games. The authors highlight the role of price value and game content quality, as well as that of positive emotions, which are always core factors when present.

To identify complex interactions among the variables of interest, fsQCA is employed, differentiating from traditional studies using variance-based methods, leading to multiple solutions explaining the same outcome. None of the variables explains the intention to download on its own, but only when they combine with each other. The authors extend existing knowledge on how price value, game content quality, emotions, gender and gameplay time combine to lead to high intention to download mobile games; and present a methodology for how to bridge complexity theory with fsQCA, improving our understanding of intention to adopt mobile applications.

The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.

This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.

The authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples.

While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.

Strategic alignment is a theory-based state that is considered as crucial for organizations in order to realize performance gains from information technology (IT) investments and deployments. Within the domain of purchasing and supply chain management there has been a growing interest on how purchasing strategy can be effectively aligned with IT and what conditions facilitate this state. The purpose of this paper is to investigate complex causal relationships of contingency elements that are key in enabling the “fit” between purchasing strategy and IT.

The paper employs a configuration theory approach and propose that purchasing alignment is dependent upon patterns of multiple contingencies. In adherence with contingency theory, the authors group these elements as relating to strategic orientation, organizational factors, and purchasing decisions. On a sample of 172 international companies the authors then apply the novel methodology of fuzzy set qualitative comparative analysis (fsQCA).

The paper empirically demonstrates that depending on the strategic orientation that a company follows, there are alternative combinations of elements that lead to high purchasing alignment. For companies following an operational excellence strategic orientation, a high contract binding scheme, or a small firm size facilitates purchasing alignment. Enabling elements for product leadership companies include a decentralized purchasing structure, a broad supplier base, and a large firm size. Purchasing alignment for customer intimacy companies is supported by a centralized purchasing structure, loose contract binding, and a large supplier base.

The findings of this study suggest that practitioners aiming to attain a state of purchasing alignment should consider a number of contingency elements in the process. The paper shows that there is equifinality in the configurations that lead to purchasing alignment. This means that attaining purchasing alignment is dependent upon various clusters of contingency elements which must be taken into account when formulating a purchasing strategy.

In contrast with past studies examining purchasing alignment as a result of the isolated impact of several antecedents, we applied a configuration theory approach to demonstrate that it is facilitated by the combined impact of a set of cause-effect relationships. In cases were non-linear and synergistic relationships exist between independent variables, this type of research may be a viable alternative.

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

Video-based learning (VBL) is gaining increased attention as an educational means in settings such as the flipped classroom and massive open online courses. The value of VBL has been recognized in a range of contexts due to the ability to extend opportunities for life-long education for all socio-economic levels, removing geographical boundaries while at the same time alleviating time constraints. Yet, despite the advantages featured by VBL and some promising early outcomes regarding its effectiveness, little is known about what influences individuals to adopt VBL systems and technologies. The paper aims to discuss this issue.

Building on behavioral and adoption-acceptance theories as well as on past empirical studies on e-learning, a conceptual model of VBL adoption is proposed. By analyzing survey data from 260 VBL learners, the conceptual model is put to test by means of structural equation modeling.

Outcomes indicate that performance expectancy (PE) and computer self-efficacy (CSEF) have a positive direct effect on behavioral intention to adopt VBL. In addition, effort expectancy (EE) and social influence positively impact PE, while CSEF is found to enhance EE and perceived behavioral control.

This study integrates several theoretical perspectives that are applied to adoption of novel information technologies and in addition builds on findings of past empirical research on learning technologies. In effect, the conceptual model along with outcomes provide an integrative perspective of adoption determinants.

While the dynamic capabilities perspective is the most cited strategic theory in the information systems field of research, little effort has been made to review and integrate the associate literature of this perspective in the field. Accordingly, this paper aims to systematically analyze the information systems literature on dynamic capabilities and provide a holistic understanding of the topical composition and trend of dynamic capabilities studies in information systems research.

Using latent Dirichlet allocation as the text analysis algorithm, the author conducted a topic modeling of the dynamic capabilities corpus in the information systems field of research to quantitatively review, summarize and classify the prior literature. The review covered 191 articles published on dynamic capabilities between 1998 and 2018 in pioneering information systems journals and conference proceedings.

In accordance with the topic modeling results, the topical composition of the dynamic capabilities corpus in information systems research dominantly includes seven themes titled T1. Information systems value, T2. Information systems change, T3. Digitalization, T4. Information systems agility, T5. Big data, T6. Information systems innovation and T7. Information systems alignment. Also, the overall and topical trend of dynamic capabilities studies in the information systems field of research were revealed. The trends indicated that the investigated domain and its prominent sub-domains have generally had positive productivity over the past years.

The current study contributes to the domain by developing knowledge and improving literature on dynamic capabilities in information systems research, discovering the main topics of interest for information systems researchers to deploying the dynamic capabilities perspective in their studies, and prioritizing the future information systems research on dynamic capabilities based on the identified trends of topics.

This chapter aims to analyze how digital entrepreneurship is developed in the food industry of the European Union, comparing digital skills and big data indicators in all enterprises and the food industry. Using Eurostat Digital Economy and Society database, the authors obtained data between 2016 and 2020 – including the indicators: information and communications technology (ICT) specialists and ICT training to digital skills, and smart devices, geolocation, and social media to big data assessment. Furthermore, we compared all enterprises with those that manufacture beverages, food, and tobacco products. The authors identified that the food sector is still behind the other sectors regarding digitalization. Consequently, this research contributes to understanding entrepreneurs’ digital skills and how them relate to the use of big data in the food industry. Moreover, it also allows identifying the digital indicators of the food industry as less innovative than other industry digital indicators.

This study aims to reveal how mobile app stickiness is formed and how the stickiness formation process differs for apps of different social levels.

This study proposed and validated a stickiness formation model following the cognitive–affective–conative framework. Data were collected from surveys of 1,240 mobile app users and analyzed using structural equation modeling. Multigroup analysis was applied to contrast the stickiness formation process among apps of different social levels.

This study revealed a causal link between cognitive, affective and conative factors. It found partial mediation effects of trust in the association between perceptions and satisfaction, and the full mediation role of satisfaction and personal investment (PI) in the effects of subjective norm (SN) on stickiness. The multigroup analysis results suggested that social media affordances benefit stickiness through increased PI and strengthened effects of SN on PI. However, it damages stickiness through increased perceived privacy risk (PPR), decreased trust and strengthened effects of PPR on trust.

This study contributes to both stickiness scholars and practitioners, as it builds a model to understand the stickiness formation process and reveals the effects of the “go social” strategy. The novelty of this study is that it examined social influences, considered privacy issues and revealed two mediation mechanisms. The findings can guide the improvement of mobile app stickiness and the application of the “go social” strategy.