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The purpose of this paper is to investigate the use contexts of personal computing devices in multiple steps and conducts an 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, χ² 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 information technology services.

The purpose of this study is to empirically investigate what aspects of service robot interactions with customers can lead to meaningful outcomes in the view of customers. The study examines functional and emotional elements of AI service robots in terms of meaningful outcomes.

This study highlights AI service robots' meaningful outcomes as a viable research problem and proposes a research model utilizing the Stimulus-Organism-Response (SOR) framework. As an empirical approach, 260 datasets were collected from customers who have experience with AI service restaurants in China.

The study examines the functional and emotional elements of AI-powered service robots on the attitude of and meaningful outcomes for customers. The results showed that the emotional (perceived friendliness and perceived coolness) and functional (perceived safety and robot competence) attributes of human–robot interactions (HRI) significantly affect the attitude toward using service robots. Second, the attitude toward using service robots significantly influences the experiential outcome and instrumental outcome of meaningful engagement.

This study highlights two elements (i.e. functional and emotional) of HRI effectiveness using two metrics: experiential and performance outcomes. Future studies should generalize the research findings of service robots in the current study using a larger quantity of data from various service fields.

As the first empirical study highlighting the customer experience with service robots, this study opens up a feasible research direction for the service industry to pursue in terms of conducting HRI studies from the view of customers. It identifies a research model pursuant to customers' experience with HRI in creating meaningful outcomes and it theoretically extends the SOR model to the hospitality study, focusing on the HRI issue.

Cloud computing has disrupted the information technology (IT) industry. Associated benefits such as flexibility, payment on an on-demand basis and the lack of no need for IT staff are among the reasons for its adoption. However, these services represent not only benefits to users but also threats, with cybersecurity issues being the biggest roadblock to cloud computing success. Although ensuring data security on the cloud has been the responsibility of providers, these threats seem to be unavoidable. In such circumstances, both providers and users have to coordinate efforts to minimize negative consequences that might occur from these events. The purpose of this paper is to assess how providers and users can rely on social media to communicate risky events.

Based on the Situational Theory of Publics and trust, the authors developed three research questions to analyze stakeholders’ communication patterns after a security breach. By gathering Twitter data, the authors analyzed the data security breach faced by the Premera Blue Cross’ Web application.

The results indicate that Premera acted as the main source of information for Twitter users, while trustworthy actors such as IT security firms, specialists and local news media acted as intermediaries, creating small communities around them. Theoretical and practical implications are also discussed.

Social media could be used for diffusing information of potential threats; no research has assessed its usage in a cloud-based security breach context. The study aims to fill this gap and propose a framework to engage cloud users in co-securing their data along with cloud providers when they face similar situations.

As the center of the fourth industrial revolution, artificial intelligence (AI) has marked its presence in various disciplines including the education field in the form of AI-powered learning applications. The purpose of this study is to build a research model capturing the relationships among use contexts, user gratification, attitude, learning performance and continuous intention to use an AI-powered English learning application.

Using the use and gratification theory, use contexts and the belief-attitude-intention theory, this paper uses a quantitative approach based on a survey method for data collection and structural equation modeling for analysis. A total of 478 students from an international university in Guangdong, China, participated in the survey after using Liulishuo for two weeks.

The results showed that perceived use contexts affected all variables associated with gratifications-obtained and gratification-opportunities. With the exception of social integrativeness, all other gratification-based factors significantly affected attitude. The attitude in turn significantly influenced learning performance and continuous use intention.

Mobile AI-powered learning applications are at the center of research on technology-enhanced learning in the age of media and technology convergence. The study is timely and contributes to the discussion of the roles of use context and gratifications on technology users’ attitudes and behavioral intentions.

Based on the post-acceptance model of information system continuance (PAMISC), this study investigates the influence of the early-stage users' personal traits (specifically personal innovativeness and technology anxiety) and ex-post instrumentality perceptions (specifically price value, hedonic motivation, compatibility and perceived security) on social diffusion of smart technologies measured by the intention to recommend artificial intelligence-based voice assistant systems (AIVAS) to others.

Survey data from 400 US AIVAS users were collected and analyzed with Statistical Product and Service Solutions (SPSS) 18.0 and the partial least square technique using advanced analysis of composites (ADANCO) 2.1.

AIVAS technology is presently at the early stage of market penetration (about 25% of market penetration in the USA). A survey of AIVAS technology users reveals that personal innovativeness is directly and indirectly (through confirmation and continuance) associated with a stronger intention to recommend the use of the device to others. Confirmation is associated with all four ex-post instrumentality perceptions (hedonic motivation, compatibility, price value and perceived security). Among the four, however, only hedonic motivation and compatibility are significant predictors of satisfaction, which lead to use continuance and, eventually, intention to recommend. Finally, technology anxiety is found to be indirectly (but not directly) associated with a lower intention to recommend.

This is the first study conducted on the early-stage AIVAS users that evaluates the influence of both personal traits and ex-post instrumentality perceptions on users' intention for continuance and recommendation to others.

Founded on the concept of organizational ambidexterity and the competing value model, the purpose of this paper is to develop an information technology (IT) ambidexterity framework to underscore the importance of a balanced and harmonious IT environment in enterprise cloud adoption.

With survey responses from 165 IT executives in a managerial position who are in charge of cloud computing implementation, partial least square method is used to test the research model.

Cloud absorptive capacity plays an important role for firms to secure a competitive advantage. The synergy of the two capabilities (flexibility and control), which have conflicting characteristics, contributes to the enhancement of cloud absorptive capacity and leads to a firm’s knowledge accumulation and performance.

This study is the very first attempt that empirically establishes the relationship between a firm’s competitiveness and cloud computing absorptive capacity. This study provides a comprehensive framework that integrates ambidexterity theory with the competing value framework (CVF) with extending the concept of absorptive capacity that is bounded within an organizational perspective into a cloud computing context.

Firms should treat cloud computing as a strategic consideration to secure a competitive advantage in the contemporary business environment. For a firm’s performance, a dual governance structure, that encompasses flexibility and control, is required to achieve competitive advantage from cloud computing adoption.

To facilitate organizational effort in achieving a harmonious cloud environment, the authors propose a comprehensive ambidexterity framework integrating the CVF approach. This framework maps IT ambidexterity onto the CVF. As CVF considers internal and external factors that ambidexterity theory does not cover, integrating two theories can provide more comprehensive implications and discussions regarding cloud computing adoption.

Social networking services (SNSs) have become deeply ingrained into our daily life. However, it has often been reported that users experience negative feelings regarding SNS usage. This phenomenon presents challenges for SNS providers in retaining or increasing their customer base. This study focuses on SNS fatigue, a negative psychological state that can lead to discontinuance intention among SNS users. This study proposes two distinctive SNS-specific contexts and investigates how they alleviate the negative effect of SNS fatigue on user behavior.

Drawing upon the context-specific theorization perspective, a model involving moderation effects of the SNS-specific contexts on the relationship between user's SNS fatigue and discontinuance intention was proposed. The model was tested using survey data of active SNS users.

The results indicate that SNS fatigue leads to user discontinuance intention. However, the SNS-specific contexts, such as social interaction context and platform service context, negatively moderate the relationship between SNS fatigue and user discontinuance intention.

The findings of this study are expected to help SNS providers develop strategies to improve their services for effective user retention.