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1 – 5 of 5Yong Rao, Meijia Fang, Chao Liu and Xinying Xu
This study aims to explore a new restaurant category’s development from birth to maturity, thereby explaining the rationale for category innovation strategies.
Abstract
Purpose
This study aims to explore a new restaurant category’s development from birth to maturity, thereby explaining the rationale for category innovation strategies.
Design/methodology/approach
The authors conducted a qualitative case study analysis of the New Chinese-style Fusion Restaurant category’s development from birth to maturity. Thematic analysis was conducted on data collected from semi-structured interviews and textual information.
Findings
A new restaurant category’s maturation is determined by the formation of society’s shared knowledge about the category’s crucial attributes, which is an outcome of market participants’ category-related social practices. The authors develop a novel, four-stage framework for the socialized construction of this shared knowledge: a knowledge creation (KC), knowledge diffusion (KD), knowledge integration (KI) and knowledge structuralization (KS). This knowledge evolution along this KC–KD–KI–KS sequence can holistically describe the category maturation process. This framework can help understand the rationale for a restaurant category’s maturation by analyzing the interrelationships among market participants’ social practices, knowledge-related activities and market development.
Research limitations/implications
This study explains how market participants’ knowledge-related activities facilitate a new restaurant category’s maturation. This can help restaurant managers cope with increasingly homogeneous competition by applying a category-innovation strategy.
Originality/value
This study extends product categorization research on restaurants by articulating a product category’s maturation process from a knowledge perspective.
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Xinying Yu, Shi Xu and Mark Ashton
The use of artificial intelligence (AI) in the workplace is on the rise. To help advance research in this area, the authors synthesise the academic research and develop research…
Abstract
Purpose
The use of artificial intelligence (AI) in the workplace is on the rise. To help advance research in this area, the authors synthesise the academic research and develop research propositions on the antecedents and consequences of AI adoption and application in the workplace to guide future research. The authors also present AI research in the socio-technical system context to provide a springboard for new research to fill the knowledge gap of the adoption and application of AI in the workplace.
Design/methodology/approach
This paper summarises the existing literature and builds a theoretically grounded conceptual framework on the socio-technical system theory that captures the essence of the impact of AI in the workplace.
Findings
The antecedents of AI adoption and application include personnel subsystem, technical subsystem, organisational structure subsystem and environmental factors. The consequences of AI adoption and application include individual, organisational and employment-related outcomes.
Practical implications
A research agenda is provided to identify and discuss future research that comprises not only insightful theoretical contributions but also practical implications. A greater understanding of AI adoption from socio-technical system perspective will enable managers and practitioners to develop effective AI adoption strategies, enhance employees' work experience and achieve competitive advantage for organisations.
Originality/value
Drawing on the socio-technical system theory, the proposed conceptual framework provides a comprehensive understanding of the antecedents and consequences of AI adoption and application in the work environment. The authors discuss the main contributions to theory and practice, along with potential future research directions of AI in the workplace related to three key themes at the individual, organisational and employment level.
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Degan Zhang, Guanping Zeng, Enyi Chen and Baopeng Zhang
Active service is one of key problems of ubiquitous computing paradigm. Context‐aware computing is helpful to carry out this service. Because the context is changing with the…
Abstract
Active service is one of key problems of ubiquitous computing paradigm. Context‐aware computing is helpful to carry out this service. Because the context is changing with the movement or shift of the user, its uncertainty often exists. Context‐aware computing with uncertainty includes obtaining context information, forming model, fusing of aware context and managing context information. In this paper, we focus on modeling and computing of aware context information with uncertainty for making dynamic decision during seamless mobility. Our insight is to combine dynamic context‐aware computing with improved Random Set Theory (RST) and extended D‐S Evidence Theory (EDS). We re‐examine formalism of random set, argue the limitations of the direct numerical approaches, give new modeling mode based on RST for aware context and propose our computing approach of modeled aware context.In addition, we extend classic D‐S Evidence Theory after considering context’s reliability, time‐efficiency and relativity, compare relative computing methods. After enumerating experimental examples of our active space, we provide the evaluation. By comparisons, the validity of new context‐aware computing approach based on RST or EDS for ubiquitous active service with uncertainty information has been successfully tested.
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Alhamzah Alnoor, Hadi Al-Abrrow, Hussam Al Halbusi, Khai Wah Khaw, XinYing Chew, Marwa Al-Maatoq and Raed Khamis Alharbi
The internet creates ample opportunities to start a mobile social commerce business. The literature confirms the issue of customer trust for social commerce businesses is a…
Abstract
Purpose
The internet creates ample opportunities to start a mobile social commerce business. The literature confirms the issue of customer trust for social commerce businesses is a challenge that must be addressed. Hence, this study aims to examine the antecedents of trust in mobile social commerce by applying linear and non-linear relationships based on partial least squares structural equation modeling and artificial neural network model.
Design/methodology/approach
This study applied a non-linear artificial neural network approach to provide a further understanding of the determinants of trust in mobile social commerce based on a non-linear and non-compensatory model. Besides, a questionnaire was distributed to 340 social commerce customers in Malaysia.
Findings
The conceptual framework for investigating trust in mobile social commerce has various advantages and contributions to predicting consumer behavior. The results of the study showed there is a positive and significant relationship between social support, presence and unified theory of acceptance and use of technology2 (UTAUT2). In addition, UTAUT2 has fully mediated the relationship between social support, presence and trust in social commerce. Finally, the results concluded the relationship between UTAUT2 and trust in social commerce would be stronger when the diffusion of innovation and innovation resistance is high and low, respectively.
Research limitations/implications
The current study provides a novel perspective on how customers can trust social m-commerce to provide real solutions to managers of encouraging e-marketing among consumers.
Practical implications
This paper shows how businesses can develop trust in social m-commerce in Malaysian markets. The findings of this study probably could be extended to other businesses in Asia or other countries. Because trust in social e-commerce has a dynamic role in consumer behavior and intention to purchase.
Originality/value
This study provided a new perspective on mobile social commerce and paid more attention to an investigation of such emerging commerce. The originality of this study is embodied by investigating an integrated model that included different theories that presented new directions of trust in mobile social commerce through social and behavioral determinants.
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The purpose of this study is to examine the effect of perceived justice and consumer's moral judgment of a service failure on recovery outcomes.
Abstract
Purpose
The purpose of this study is to examine the effect of perceived justice and consumer's moral judgment of a service failure on recovery outcomes.
Design/methodology/approach
The research model is examined by adopting a field study approach followed by an experiment. The SPSS program with the PROCESS tool was used to analyze the simple moderation and moderated mediation effects.
Findings
The research findings show that consumer's moral judgment of a service failure moderates the relationship between service recovery (psychological compensation vs monetary compensation) and recovery outcomes (recovery satisfaction, negative word of mouth and repurchase intention). Moreover, the conditional indirect effect of service recovery on recovery outcomes through perceived justice is significant when service failure is seen as less moral. Specifically, consumers report lower perceived justice and react negatively to recovery measures when service failure is seen as less moral. In contrast, when consumers perceive a service failure as moral, a psychological compensation outperforms a monetary compensation, lessening negative word of mouth (NWOM).
Originality/value
These findings provide important insights into recovery measure development when considering consumer moral perspectives.
Details