Search results

Few studies on hospitality firm survival consider the impact of online media exposure. This paper aims to investigate how the online news coverage of restaurants, characterized in terms of the number of articles, channel (Web page or mobile app), topic (operations or products) and consistency (mix of news tones), influences their survival.

A yearly panel data set covering 682 news-reported restaurants in Shanghai, China, over the period 2011–2019 is analyzed using a Cox model, and an extended cross-sectional data set containing 9,488 restaurants is used for robustness checks.

A larger number of online news articles, regardless of channel or topic, significantly improves restaurants’ chances of survival, and this positive impact of online exposure is greater if that news is published by mobile apps (rather than on Web pages) or reports topics related to operations (rather than products). Although, generally, news inconsistency is not good for restaurant survival, when the number of online news items is eight or more, inconsistency becomes good for survival.

This research guides restaurant operators to use news exposure in an online marketing environment to increase the firm’s chances of long-term survival.

Online media exposure has hitherto been ignored in the literature on the survival of hospitality firms. This paper provides a new perspective on hospitality firm survival and also contributes to the literature on media exposure by conceptualizing a unique factor, namely, the consistent online exposure.

The environment in high-tech industries is highly dynamic, and after COVID-19, it has become even more unpredictable. Hence, it has become critical for firms to develop strategies to cope with a highly dynamic environment. This paper aims to analyze how the impact of the scientific collaboration networks with URIs (universities and research institutes) on firm innovation performance is contingent on technological and market dynamics.

Using a sample of 174 Chinese firms in the new-energy vehicle industry during 2004–2015, the authors applied a random-effects negative binomial modeling approach to model these relationships.

A broad and strong scientific collaboration network promotes firm innovation network effects are contingent on technological and market dynamics. While technological dynamics strengthen the effect market dynamics weaken it due to the different purposes of collaboration for firms and URIs.

Firms should adjust the structure of scientific collaboration networks with URIs when facing different environments. The government should encourage firms to jointly research with diverse URIs and play an active role in stabilizing market environments.

This study contributes to the academic debate on university-industry scientific collaborations. Applying the temporary competitive advantage (TCA) framework, we provide nuances to the literature that studies the factors that condition the effects of networks. This study also adds to the research on firm scientific collaboration networks by measuring networks based on the coauthorship between firms and URIs.

The purpose of this paper is to develop a novel method for analyzing wheel-rail (W-R) contact using thermo-mechanical measurements and study the effects of heating on the characteristics of W-R contact under different creepages.

This study developed an implicit-explicit finite element (FE) model which could solve both partial slip and full sliding problems by setting different angular velocities on the wheels. Based on the model, four material types under six different creepages were simulated.

The results showed that frictional heating significantly affected the residual stress distribution under large creepage conditions. As creepage increased, the temperature of the wheel tread and rail head rose and the peak value was located at the trailing edge of the contact patch.

The proposed FE model could reduce computational time and thus cost to about one-third of the amount commonly found in previous literature. Compared to other studies, these results are in good agreement and offer a reasonable alternative method for analyzing W-R contact under various conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2019-0298

This study examines how top global brands changed their corporate social responsibility (CSR) communication on social media during a victim crisis, and how their CSR communication on social media influenced consumer sentiment.

Using 18,502 firms’ Facebook posts and their most relevant consumer comments from pre-pandemic and during-pandemic timeframes, this study integrates machine learning techniques (BERTopic) with human-based qualitative analysis to analyze CSR posts. It also measures the polarity and magnitude of consumer sentiment with Google Natural Language AI. We tested seven hypotheses using Hierarchical Linear Modeling (HLM).

The machine learning-based topic modeling analysis showed that firms increased CSR communications intensity on social media and they more intentionally chose different CSR communication strategies for different topics on social media during the victim crisis. The hypothesis testing results show proactive, accommodative and interactive strategies have a significant impact on consumer sentiment polarity and magnitude, and these effects are moderated by the level of interactivity and industry type.

(1) This study takes a dynamic view to examine the firms’ CSR communication on social media during a victim crisis. It used machine learning-based text analytics and found many interesting results on how firms changed their CSR communication topics and strategies on social media during the crisis. (2) It measures both consumer sentiment polarity and sentiment magnitude to conduct sentiment analysis. The results indicate that the CSR communication strategies have different impacts on the two sentiment components. (3) It integrates machine learning techniques with human-based qualitative analysis. It shows how researchers can gain the benefits of both approaches.

This paper investigates whether a J-curve can be detected in the time series data on China’s bilateral trade with the G-7 countries. It utilizes cointegration and causality tests to ascertain both the long-run relatedness, and the short-run dynamics, between the real exchange rate, national income, and the trade balance. There is some evidence that a real depreciation eventually improves the trade balance with some countries. But there is no indication of a negative short-run response which characterizes the J-curve.

The purpose of this paper is to examine the harmful effect of abusive supervision on employee creativity through feedback-seeking behavior (FSB) and the moderation of this mediation by proactive personality.

This study conducted hierarchical regression and path analysis to analyze the 341 manager–employee dyads data gathered from 11 companies in China.

The results reveal that abusive supervision had a detrimental effect on employee creativity partially mediated by employee FSB, and employees' proactive personality moderated the mediation.

Although the findings of this research are based on multiple sources data, it is hard to draw causal inferences because the nature of the data is cross-sectional.

Organizations should be aware of the deleterious effect of abusive supervision on employee creativity and should seek to inhibit supervisors' abusive behavior in the workplace.

This study re-examines the relationship between abusive supervision and employee creativity in the Chinese context from the social exchange perspective. The mediating role of FSB and the moderating role of proactive personality offer new insights in-to the mechanisms and boundary conditions associated with the relationship between abusive supervision and employee creativity.

The purpose of this study is to solve the problem that existing researches ignore the long-term and staged nature of digital transformation, failing to conduct specific discussions for different stages. It responded the call by constructing a three-stage evolutionary model to analyze the impact of digital transformation at different stages on the sustainable performance of manufacturing enterprises. The moderating effect of core technology capabilities is also explored, guided by the theory of assimilation innovation.

Based on the panel data of Chinese listed manufacturing companies from 2012 to 2020, this study empirically investigate the impact of digital transformation (digital process, digital operation and digital ecology) on sustainability performance (economic performance and environmental performance).

The findings indicate that digital operations and digital ecology significantly improve economic performance and environmental performance. Furthermore, the core technological capacity of the enterprise serves to modify the positive correlation between digital transformation at each stage and sustainable performance to some extent. In other words, when an enterprise is equipped with the requisite technological capacity, the digital transformation at each stage accelerates both economic performance and environmental performance, which in turn is conducive to an improvement in the enterprise’s sustainable development performance.

The findings contribute to the theoretical framework of digital transformation and sustainable development in all stages of enterprises. Furthermore, they provide guidance for achieving sustainable development through the implementation of digital transformation and the enhancement of core technological capacity.

The purpose of this paper is to explore the restrictive effect of abusive supervision on employees’ feedback-seeking behavior (FSB) through organizational-based self-esteem (OBSE) and the moderation of this mediation by leader-member exchange (LMX).

This study conducted hierarchical regression and path analysis to analyze the 312 manager–employee dyads data gathered from five companies in China.

The authors found that abusive supervision had a detrimental effect on employee FSB, partially through OBSE, and that both the direct and indirect effects were moderated by LMX.

Organizations should seek to inhibit supervisors’ abusive behavior in the workplace. Supervisors should not occasionally mistreat subordinates with whom they have a good relationship.

This study reveals the underlying influence mechanism of abusive supervision on employee FSB using the self-concept theory and suggests that OBSE is critical in determining how abusive supervision influences employee FSB. Furthermore, LMX quality (especially high LMX) moderates the above mediation.

This study explores how perceived interactivity effects in Livecommerce influences consumers' shopping intentions in live stream commerce. The authors specifically examine the mediating roles of immersion and hedonic/utilitarian gratification, as well as the moderating role of product involvement in identifying the boundary conditions.

A scenario-based online survey was conducted among American consumers via Prolific.co, an online crowdsourcing platform. The final sample included 187 respondents (male, 63.1 per cent; Caucasian, 61.5 per cent).

The results indicate that perceived interactivity significantly influenced consumers' shopping intentions. Consumers' sense of immersion and hedonic/utilitarian gratification were identified as serial mediators between perceived interactivity and consumers' shopping intentions. It was also found that individuals' product involvement moderated the serial effects of perceived interactivity on consumers' shopping intentions in live stream commerce.

This study provides empirical evidence that perceived interactivity plays an important role in creating an effectively immersive media experience in live stream commerce, which further contributes to higher shopping intentions via perceived utilitarian and hedonic gratifications. It was also found that varying levels of product involvement can have differing effects. Managerial implications are provided.

To explore the psychological mechanism through which consumer reviews affect people’s purchasing decisions and behavior, this study aims to examine the impact of statistical evidence embedded in product reviews on consumers’ perceptions and purchasing intentions.

The effects review valence and review volume are tested using a 3 (valence: positive vs neutral vs negative) × 2 (volume: high vs low) quasi-experimental design and online questionnaires.

The study finds that review valence has a stronger impact on consumers’ perceptions than review volume does. Negative reviews induce higher risk perception and a less favorable attitude toward purchases compared to positive reviews. In addition, although both attitude toward purchase and subjective norm are good antecedents of purchase intention, the attitude statistically has a stronger impact than the subjective norm.

This study contributes to extant literature from three perspectives. The authors have reexamined the findings of econometric models and advanced their implications by explaining the related psychological changes in people’s perceptions. Second, the authors have extended the application of the theory of reasoned action and found it to be a good fit in explaining consumers’ behavior related to consumer reviews. And finally, the authors have provided a clear guideline on the magnitude of the effects of review valence and volume on consumers’ perceptions.

This study provides a good complement to econometric studies from both theoretical and practical perspectives. It bridges the gap between exploratory studies and behavioral studies in the field of consumer reviews.