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1 – 5 of 5Ping Li, Siew Fan Wong, Shan Wang and Younghoon Chang
This study aims to study the mechanisms and conditions of users' intention to continue to use online health platforms from an information technology (IT) affordance perspective.
Abstract
Purpose
This study aims to study the mechanisms and conditions of users' intention to continue to use online health platforms from an information technology (IT) affordance perspective.
Design/methodology/approach
b This research proposes that a critical affordance effect on an online health platform, users' intention to continue the use of the platform, is affected by five platform affordances via two actualized affordances (i.e. perceived benefits (PBs) and online engagement (OE)). Perceived health threat moderates the effect generated by affordance actualization. A dataset involving 409 users from the “Ping An Health” platform was collected through an online survey and analyzed to validate the research hypotheses.
Findings
The data analysis results confirm that the proposed online health platform affordances affect users' PBs and OE, which influence users' intentions to continue using the platform. Perceived threats (perceived vulnerability (PVU) and perceived severity (PSE)) moderate the relationship between PBs and continuance intention (CI) and between OE and CI.
Practical implications
The research provides important recommendations for online health platform designers to develop IT affordances that can support users' needs for healthcare services.
Originality/value
Limited studies investigated why users continue participating in online diagnosis and treatment. This study provides a new perspective to expand the affordance framework by combining technology features and user health behavior. The study also emphasizes the importance of perceived threats in IT use.
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Sunyoung Hlee, Jaehyun Park, Hyunsun Park, Chulmo Koo and Younghoon Chang
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
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Seoyoun Lee, Younghoon Chang, One-Ki Daniel Lee, Sunghan Ryu and Qiuju Yin
This study explores the key platform affordances that online social platform providers need to offer digital creators to strengthen the creator ecosystem, one of the leading…
Abstract
Purpose
This study explores the key platform affordances that online social platform providers need to offer digital creators to strengthen the creator ecosystem, one of the leading accelerators for platform growth. Specifically, it aims to investigate how these affordances make the dynamic combinations for high platform quality across diverse platform types and demographic characteristics of digital creators.
Design/methodology/approach
This study adopts a multi-method approach. Drawing upon the affordance theory, Study 1 aims to identify the key affordances of online social platforms based on relevant literature and the qualitative interview data collected from 22 digital creators, thereby constructing a conceptual framework of key platform affordances for digital creators. Building on the findings of Study 1, Study 2 explores the dynamic combinations of these platform affordances that contribute to platform quality using a configurational approach. Data from online surveys of 185 digital creators were analyzed using fuzzy set qualitative comparative analysis (fsQCA).
Findings
The results of Study 1 identified key online social platform affordances for digital creators, including Storytelling, Socialization, Design, Development, Promotion, and Protection affordance. Study 2 showed that the combinations of these platform affordances for digital creators are diverse according to the types of platforms, creators’ gender, and their professionality.
Research limitations/implications
Like many studies, this research also has several limitations. One limitation of the research is the potential constraint of the extent of how well the data samples represent the group of creators who are actively producing digital content. Despite the addition of screening questions and meticulous data filtering, it is possible that we did not secure sufficient data from creators who are actively engaged in creative activities. In future research, it is worth contemplating the acquisition of data from actual groups of creators, such as creator communities. Future researchers anticipate obtaining more in-depth and accurate data by directly involving and collaborating with creators.
Practical implications
This study highlights the need for online social platforms to enhance features for storytelling, socializing, design, development, promotion, and protection, fostering a robust digital creator ecosystem. It emphasizes clear communication of these affordances, ensuring creators can effectively utilize them. Importantly, platforms should adapt these features to accommodate diverse creator profiles, considering differences in gender and expertise levels, especially in emerging spaces like the Metaverse. This approach ensures an equitable and enriching experience for all users and creators, underlining the importance of dynamic interaction and inclusivity in platform development and creator support strategies.
Social implications
This study underscores the social implications of evolving digital creator ecosystems on online platforms. Identifying six key affordances essential for digital creators highlights the need for platforms to enhance storytelling, socializing, design, development, promotion, and product protection. Crucially, it emphasizes inclusivity, urging platforms to consider diverse creator profiles, including gender and expertise differences, particularly in transitioning from traditional social media to the Metaverse. This approach nurtures a more robust creator ecosystem and fosters an equitable and enriching experience for all users. It signals a shift towards more dynamic, adaptive online environments catering to diverse creators and audiences.
Originality/value
For academics, this study builds the conceptual framework of online social platform affordances for digital creators. Using the configurational approach, this study identified various interdependent relationships among the affordances, which are nuanced by specific contexts, and suggested novel insights for future studies. For practices, the findings specified by creators and platform types are expected to guide platform providers in developing strategies to support digital creators and contribute to platform growth.
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Youyung Hyun, Jaehyun Park, Taro Kamioka and Younghoon Chang
The current study aims to structure the existing knowledge about organizational agility from the information systems (IS) capabilities view and synthesizes how agility is enabled…
Abstract
Purpose
The current study aims to structure the existing knowledge about organizational agility from the information systems (IS) capabilities view and synthesizes how agility is enabled by big data analytics (BDA).
Design/methodology/approach
This study performs a systematic literature review with the lens of IS capabilities view and provides an integrative framework that represents how BDA improves organizational agility through the mediation of IS capabilities.
Findings
This systematic literature review synthesizes what is known and identifies what remains to be further studied with a focus on the relationship between BDA competency and organizational agility, which contributes to academic performance in BDA and agility research communities.
Originality/value
Despite a growing body of literature on the relationship between BDA and agility, a consolidated and systematic understanding of how BDA can enable organizational agility is generally missing. Therefore, the current study addresses this gap by proposing an integrative framework that elucidates the processes in which BDA competency leads to agility through the mediation of IS capabilities.
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Zhao Dong, Ziqiang Sheng, Yadong Zhao and Pengpeng Zhi
Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic…
Abstract
Purpose
Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.
Design/methodology/approach
The MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.
Findings
The prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.
Originality/value
The MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.
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