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The motel industry in Taiwan is a highly competitive industry, which requires considerable investment and demands innovative services. This paper aims to identify the competitive qualities that urgently require improvement to enhance the service quality of motels using the two-phase method of importance-performance and gap analysis (IPGA) and the Kano model, and develops specific improvement strategies.

By questionnaire survey, this study collects 562 valid questionnaires from motels in Taipei City. The IPGA and Kano model are used to identify the service attributes of market competitiveness that urgently require improvement.

The results show there are 14 attributes that should be urgently improved, of which four are the attractive qualities of market competitiveness (competitive qualities). This study develops ten improvement strategies, as based on the four competitive qualities, for the reference of the motel industry.

The results can identify the competitive qualities that require urgent improvement to address the development of improvement strategies for motels. The managers of motels can refer to the ten improvement strategies to create excellent and competitive motel services.

The results combine “customers’ needs” with “the competitive meanings of quality attributes in the market” under limited resources, to upgrade motel service quality, customers’ willingness to purchase and motel competitiveness.

In the tide of the sharing economy, food and beverage sharing services (FBSS) are gradually drawing public attention. Many comments about FBSS are posted and discussed online, and this information may suggest the key factors in the operation of FBSS. This study aims to identify the key success factors (KSFs) of FBSS from online communities and media, potential consumers, customers and experts.

This study utilizes Internet big data analytics (IBDA) to identify the key FBSS factors and then examines the KSFs of FBSS through conducting an analysis of the importance of key factors for potential consumers, confirmatory factor analysis of customer satisfaction of key factors, multiple regression analysis of customer satisfaction of key factors influencing the customers' intentions to continue participating in FBSS and a decision-making trial and evaluation laboratory of experts' opinions.

The results showed that the 15 key FBSS factors through IBDA were screened out. Among them, four KSFs that influence the operation of FBSS were identified. These four KSFs are discussed in detail in the text.

The findings of this study provide references for FBSS providers in the future to enhance customer value, service quality and business competitive advantages of FBSS.

This study aims to develop a comprehensive LOHAS (lifestyles of health and sustainability) restaurant service quality scale by using a rigorous qualitative and quantitative research process to effectively measure the service quality of LOHAS restaurants. Moreover, this study aims to further identify the Kano quality characteristics and strategic meanings of service attributes in LOHAS restaurants.

This study designed the preliminary items of the service quality scale for LOHAS restaurants (LORSERV scale) based on relevant literatures and expert interview procedures. This study identified the goodness of fit of the questionnaire content, construct validity and validity of the LORSERV scale using exploratory factor analysis and confirmatory factor analysis. The moderated regression was conducted to identify the Kano quality characteristics and strategic meanings of each service attribute in LOHAS restaurants.

The results indicated that the LORSERV scale included seven dimensions (internal sense of happiness, transitiveness, environment, healthy catering, service commitment, green practicability and thoughtfulness), for a total of 33 items. According to the results of the Kano model, the seven service attributes were categorized into the attractive quality. A total of 25 service attributes were categorized into the one-dimensional quality, and one service attribute was categorized into the must-be quality.

The contribution of this study is that the scale could facilitate operators of LOHAS restaurants to effectively understand customer perceptions of service quality and serve as a reference to upgrade and improve service quality. The identification of Kano quality characteristics for each service attribute is conducive for LOHAS restaurants to understand the strategic meanings of each service attribute and can serve as a reference to make distinctive service strategies to reach sustainable operations.

The purpose of this paper is to explore the structural relationship between green persistence intentions and their seven drivers – green authenticity, green perceptual evaluation, green co-creation, green experiential memorability, green experiential satisfaction, green passionate love and green need for cognition in a green hotel context.

The data used in this study were based on a sample of 589 customers staying at one green hotel in New Taipei City of Taiwan. The predicted relationship was tested using the structural equation modeling and the hierarchical regression analysis.

The results indicate that green perceptual evaluation, green co-creation and green experiential memorability influence green experiential satisfaction. Green passionate love is influenced by green experiential satisfaction. Green need for cognition moderates the effect of green experiential satisfaction on green persistence intentions. Green persistence intentions are influenced by green experiential satisfaction and green passionate love.

To increase green authenticity, green perceptual evaluation, green co-creation, green experiential memorability, green experiential satisfaction, green passionate love, green need for cognition and green persistence intentions, the study findings will help green hotels to develop and implement market-orientated product and/or service strategies.

This paper provides data that lead to a better understanding of the relationships among green authenticity, green perceptual evaluation, green co-creation, green experiential memorability, green experiential satisfaction, green passionate love, green need for cognition and green persistence intentions in a green hotel context.

This study aims to examine the effects of coworker incivility on employees' behaviors using a moderated mediation model that conceptualizes coworker exchange (CWX) as a mediator and servant leadership as a moderator.

The data were collected using a multi-temporal research design. The hypotheses were tested on a sample of 1,272 participants using confirmatory factor analysis (CFA), hierarchical regression analysis and moderated path analysis. In addition, supervisor incivility was added as a control variable to partial out the potential influence on employees' behaviors.

The results of CFA ensured that all measures had discriminant and convergent validity. In addition, the results of hierarchical regression analysis and moderated path analysis indicated that CWX mediates the relationship between coworker incivility and employees' behaviors. Furthermore, servant leadership exacerbates the negative relationship between coworker incivility and CWX.

Leaders and practitioners should invest in communication training programs for developing employees' communication skills to avoid incivility. In addition to viewing incivility as inappropriate behavior, leaders and practitioners should understand the meaning beyond those incivilities.

This study utilized incivility spiral theory to examine how coworker incivility affects employees' behaviors. The mediated path analysis found that CWX mediates the relationship between these variables, which has been ignored by previous research. Furthermore, this study introduced servant leadership as a moderator to account for the “when” in incivility spiral theory, i.e. what kind of social context facilitates or inhibits the influence of coworker incivility on CWX.

This study focused on the impact of misinformation on social networking sites. Through theorizing and integrating literature from interdisciplinary fields such as information behavior, communication and relationship management, this study explored how misinformation on Facebook influences users' trust, distrust and intensity of Facebook use.

This study employed quantitative survey research and collected panel data via an online professional survey platform. A total of 661 participants in the USA completed this study, and structural equation modeling (SEM) was used to test the theoretical model using Amos 20.

Based on data from an online questionnaire (N = 661) in the USA, results showed that information trustworthiness and elaboration, users' self-efficacy of detecting misinformation and prescriptive expectancy of the social media platform significantly predicted both trust and distrust toward Facebook, which in turn jointly influenced users' intensity of using this information system.

This study contributes to the growing body of literature on information and relationship management and digital communication from several important aspects. First, this study disclosed the underlying cognitive psychological and social processing of online misinformation and addressed the strategies for future system design and behavioral intervention of misinformation. Second, this study systematically examined both trust and distrust as cognitive and affective dimensions of the human mindsets, encompassed the different components of the online information behavior and enriched one’s understanding of how misinformation affected publics' perceptions of the information system where it appeared. Last but not least, this study advanced the relationship management literature and demonstrated that a trustful attitude exerted a stronger influence on the intensity of Facebook use than distrust did.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2020-0130

The categorization response model through gene expression patterns turns into one of the most favorable utilizations of the microarray technology. In this study, the aim is to propose a grid computing-based meta-evolutionary mining approach as a categorization response model for gene selection and cancer classification.

The proposed approach is based on the grid computing infrastructure for establishing the best attributes set selected from a big microarray data. The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.

Examples for several benchmarking cancer microarray data sets were used to evaluate the proposed approach, whose results are also compared with other approaches in literatures. Experimental results from four benchmarking problems indicate that the proposed approach works effectively and efficiently, and the results of the proposed methods are superior to or as well as other existing methods in literatures.

The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach to discover the best feature subset automatically from the microarray tumor database. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

This study aims to conduct a critical review of the research on the sharing economy to identify its key intellectual foundations and their evolution and offers thematic and methodological recommendations for future research to advance the domain.

A tri-method approach using bibliometric (co-citation) analysis, thematic content analysis and a quantitative systematic literature review was conducted on sharing economy research in hospitality and tourism journals, up to and including May 2020.

The findings from the three methods were coherent and provide a clear picture of the fact that while research on the sharing economy in hospitality and tourism has achieved significant depth, the breadth of the understanding of this area remains somewhat limited. Each of the three periods of research on the sharing economy identified in the study were marked by a focus on specific thematic areas, with largely Western-situated researchers demonstrating limited theoretical engagement and using a limited range of methods and perspectives (disciplinary, stakeholders and sectors) to examine these themes.

Recommendations for future thematic research opportunities are provided using a multi-level perspective. The present review does not include research that incorporates the impacts of COVID-19, which has significantly disrupted the hospitality and tourism industry and is the focus of current research in the field. However, the review represents the largest and most comprehensive assessment of the state of research on the sharing economy prior to COVID-19, and, as such, can serve as a valuable baseline for future reviews of sharing economy research during and after the pandemic.

In contrast with previous literature reviews, the present review is comprehensive in its scope, methodology and temporal coverage of sharing economy research. It also examines the evolution of research on the topic, enabling a more nuanced identification of gaps and future thematic and methodological research opportunities.