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To create the expected value and benefits through open data, appropriate provision and usage of data are required simultaneously. However, the level of provision and usage of open data differs from country to country. Moreover, previous research on open data has only focused on either open data provision or usage. To fill the research gap, the purpose of this paper is threefold: first, to understand the current status of the provision and usage of open data; second, to identify patterns in the provision and usage of open data; and third, to provide appropriate future directions and guidelines for the transformation paths of each pattern.

The authors analyzed the data collected from open data portals of 13 countries that provide information on the provision and usage of open data together.

The authors identified four patterns of the provision and usage of open data, namely, availability-driven, government-driven, market-driven and interaction-driven patterns. Furthermore, three strategic paths of transformation reach a high level of open data provision and usage, namely, data provision-focused, data usage-focused and balanced transformation paths.

This study provides a foundation that enables researchers to build a holistic theory that can integrate fragmented and incomplete knowledge of open data and usage, particularly in the context of government.

In the data economy era, despite the tremendous effort of governments to actively provide and use open data, its effect on national performance such as competitiveness differs widely from country to country. A sufficient knowledge base and its appropriate management are important to effectively derive the potential value from open data. A country can implement multiple and equally viable means to effectively align open data with knowledge management, which lead to high national performance. However, previous studies lack consideration of the possibility of these various configurations. To fill the research gap, this study aims to investigate the configurational patterns constituted by government data openness and knowledge management for national competitiveness.

From the open innovation perspective, this study collected data from the global reports of 76 countries and examined them through fuzzy-set qualitative comparative analysis (fsQCA).

Four configurational patterns are identified, namely, coupled (outbound-focused)-, coupled (inbound-focused)-, inbound-focused-, and outbound-focused national competitiveness.

This study provides a foundation that enables researchers to build a holistic and balanced perspective that can manage open government data and develop knowledge management capability.

Many studies have investigated the relationship between the adoption of open innovation and performance in large firms. However, limited research is available with regard to the use of open innovation in small and medium enterprises (SMEs). SMEs are important because of their contribution to innovation in almost all economies. The purpose of this paper is to extend the current literature by focusing on SMEs. Using complementarity and knowledge-based theories, this study develops three hypotheses to identify the effect of knowledge sourcing approaches for innovation on SMEs’ innovation performance.

Surveys collected from 196 SMEs in Korea were analyzed using the supermodularity function to test the hypotheses.

Results indicate that an external knowledge-oriented approach has no significant effect, whereas an internal knowledge-oriented (i.e. closed) approach has a positive effect on innovation performance. Interestingly, this study found that open innovation has a negative effect on SMEs’ innovation performance (i.e. both internal knowledge-oriented and external knowledge-oriented approaches have a substitutive relationship).

This study sheds new light on open innovation and knowledge management research by identifying the relationship between knowledge sourcing approaches for innovation, and innovation performance in SMEs. Practical implications highlight that open innovation could impede SMEs’ innovation performance.

Feedback-seeking behavior is an important way for individuals to actively seek information feedback to achieve individuals' goals. In the environment driven by contactless digital technologies, the way of individual feedback-seeking behavior through monitoring indirectly becomes obvious, especially for people who complete the work online in digital collaboration. However, previous empirical research on feedback-seeking behavior mainly focused on direct inquiry. The purpose of this paper is to verify the impact of individual learning goal orientation and the digital feedback environment on individuals' feedback-seeking behaviors through inquiry and monitoring approaches. And the moderating effect of time pressure on these relationships was also investigated.

Based on socio-technical system theory, this study proposes a model to describe the formation of the two approaches of feedback-seeking behaviors (inquiry and monitoring). The hypotheses were examined with the structural equation model method and data were collected from 152 graduate students who completed online surveys.

The results show that both the digital feedback environment and learning goal orientation can promote individual inquiry and monitoring approaches of feedback-seeking. Furthermore, time pressure moderates the relationship between the digital feedback environment and feedback monitoring negatively.

This study establishes an antecedent model that influences the choice of feedback-seeking approaches in digital environments from the perspective of a socio-technical system. The empirical results supplement the explanation of the influence of both technical and social factors on individual feedback-seeking behavior in digital environments.

Facial recognition payment is an emerging mobile payment method that uses human biometrics for personal identification. The purpose of this study is to examine how users' salient beliefs regarding the technology–organization–environment–individual (TOE–I) dimensions affect their attitudes and how attitudes subsequently influence the intention to use facial recognition payment in offline contactless services.

This study comprehensively investigates customers' decision-making psychological mechanism of using facial recognition payment by integrating the belief–attitude–intention (B–A–I) model and the extended TOE–I framework. Data from 420 valid samples were collected through an online survey and analyzed using partial least squares structural equation modeling.

Research results indicate that convenience and perceived herd exert positive effects on trust and satisfaction. Meanwhile, familiarity has a significantly positive effect only on trust but not on satisfaction. In contrast, perceived privacy risk exhibits a negative effect on both trust and satisfaction. Trust and satisfaction positively influence the intention to use facial recognition payment. Unexpectedly, self-awareness negatively moderates the effect of satisfaction on intention to use, but its effect on the relationship between trust and intention to use is non-significant.

To the best of the authors’ knowledge, this study is one of the early studies that explicate customers' psychological mechanism in facial recognition payment in offline contactless services through an understanding of the B–A–I causal linkages with the identification of users' perceptions from a comprehensive context-specific perspective. This study enriches the literature on facial recognition payment and explores the moderating role of self-awareness in the relationship between users' attitudes and intention to use, thereby revealing a complex psychological process in the usage of offline facial recognition payment systems.

This research created a theoretical framework based on theory of consumption values (TCV) and theory of perceived risk (TPR) to investigate the determinant factors behind consumers' intention to use health and fitness apps during the COVID-19-related lockdown. In addition, based on selectivity hypothesis theory (SHT), this study also explored how gender differences moderate the relationships between the determinants and consumers' behavioral intention.

A total of 613 respondents completed a self-reported online questionnaire. Structural equation modeling was conducted to test the role of potential determinants in influencing consumers' behavioral intention. Hierarchical multiple regression was performed to examine the moderating effect of gender.

The findings of this research revealed that physical appearance, general health, enjoyment, affiliation and condition have positive influences on consumers' behavioral intention, while privacy risk and security risk exert negative impact on consumers' behavioral intention. More importantly, the moderating results indicated that only affiliation, privacy risk and security risk have stronger influences on female, while other predictors showed the same effects on both genders.

Fitness providers should embrace health and fitness apps as a new contactless tool to offer services during and after the COVID-19-related lockdown. Fitness providers and app developers need to focus more on the utility and quality of their health and fitness apps. In addition, they should add more gamification elements to health and fitness apps because these elements could increase consumers' hedonic experience especially during the lockdown. Third, the security systems in health and fitness apps should be continuously updated to decline privacy risk during and after the COVID-19-related lockdown. Lastly, when female consumers are targeted during the lockdown, fitness providers should make more efforts to imbue health and fitness apps with more social features and improve the level of security.

Although the importance of contactless technologies has been highlighted ever since the beginning of the COVID-19 pandemic, there has been very little research on the usage of health and fitness apps during the lockdown based on TCV and TPR. Meanwhile, the moderating role of gender differences in this context remains underexplored. This research is one of the early attempts to fill in these gaps. The findings of this study will enhance the theoretical framework regarding the acceptance and use of health and fitness apps; it also challenges the generalizability of SHT in the context of the COVID-19-related lockdown. Moreover, several important implications for the health and fitness industry during and after the COVID-19 pandemic were suggested.

This study aims to propose and validate a model for e-Learning success based on students’ experiences in the “new normal.” To achieve this goal, this study focused on answering three research questions: (1) What are the students’ experiential factors that impact e-Learning? (2) How do these experiential factors affect e-Learning success? (3) In what ways does a multimethod provide a comprehensive perspective and an in-depth understanding of students’ e-Learning experiences in the new normal?

This study applied a mixed-methods sequential approach using exploratory, confirmatory and complementary studies. First, this study undertook a text-mining exploratory analysis of the review data to extract e-Learning topics. Then, based on the Information Systems (IS) success model, this study identified an integrated framework drawn from the results of the text-mining analysis. Second, this study proposed an e-Learning, experience-based success model and corresponding hypotheses and conducted a confirmatory study with surveys to validate the model. Third, this study conducted in-depth interviews to better identify the phenomenon of interest.

The five factors extracted from the first stage are system quality, lecture content, teaching quality, online interaction and achievement. This study subsequently confirmed the significant relationships between the e-Learning success factors in the second stage based on the IS success model. Finally, a complementary study identified the importance of interactivity for e-Learning success in the new normal.

To the best of the authors’ knowledge, this paper is the first to develop an e-Learning success model using a comprehensive mixed-methods approach.

This study aims to apply the information system success model (ISSM) to examine the relationships among actual use, use continuance intention, user satisfaction and net benefits in the context of quick-service restaurant (QSR) patrons using two contactless technologies (CT): self-service kiosks (SSK) and mobile applications (MA) for food ordering. The study also investigates the moderating roles of social interaction anxiety (SIA) and language proficiency (LP) in the abovementioned relationships.

Survey data from 421 QSR patrons with experience using McDonald's SSK and MA were collected and analyzed through a seemingly unrelated regressions (SUR) technique.

Research findings reveal positive associations among actual use, use continuance intention and satisfaction with CT (i.e. SSK and MA). The actual use and satisfaction with CT are positively associated with individual benefits, leading to improved patron satisfaction with QSR. Findings also reveal that, in the case of MA, SIA positively moderates relationships between actual use/satisfaction and individual benefits and between satisfaction and organizational benefit, while LP shows negative moderating effects on those relationships.

This study is one of the first attempts to present empirical evidence of constructs in the ISSM (actual use, use continuance intention, satisfaction and individual/organizational benefits) in the context of QSR patrons using SSK and MA. It also shows that using MA can address some patrons' psychological problems interacting with others in their food-ordering processes.

Today, contactless businesses are becoming part of the “new normal” in daily life. Augmented reality-based services (ARBS) thus provide a mechanism for contactless commerce, offering customers access to sensory experiences, especially during the COVID-19 pandemic. However, privacy can be a key concern when consumers decide whether to continue using ARBS. Thus, drawing on the Appraisal Tendency Framework (ATF), the study aims to examine how augmentation quality (Aug-Q), discrete emotions (joy and frustration) and privacy perceptions influence users' ARBS continuing use intention.

A survey methodology with a well-designed online questionnaire was used for data collection. The data were analyzed using a structural equation model with Amos v. 22.0 software.

This study demonstrated that Aug-Q had a significant positive impact on joy and a significant negative impact on frustration. Additionally, joy was positively associated with the perception of privacy benefits and ARBS continuing use intention, while frustration was negatively associated with the perception of privacy benefits and ARBS continuing use intention. The results also indicate that (perceived privacy risks) PPR–benefits predict the likelihood of ARBS continuing use intention.

This study enhances understanding of users' ARBS continuing use intention from an integrative perspective based on the ATF, thus identifying the Aug-Q-induced emotions that subsequently influence privacy trade-offs and predict users' ARBS continuing use intention. The results provide evidence that privacy and emotions can be key determinants when consumers decide whether to continue using ARBS. The findings of this research may be beneficial for commercial companies in preventing the loss of ARBS users.