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Working-from-home (WFH) models represent one of several types of flexible work practices gaining ground in the context of the COVID-19 pandemic. Of particular interest is the increase in the use of digital technology platforms for work collaboration and communication. These have been largely well received in terms of their potential to mitigate disruptions to business activity and employee work life in the absence of in-person work contexts. Research indicates that the sales and adoption of many digital platforms have witnessed sharp increase since the advent of the COVID-19 pandemic. These have contributed to creating seamless organisational collaborations, shared access to electronic data and new organisational processes to mirror previous in-person work arrangements. Many organisational members have had to upskill at rapid rates to catch up with these developments. Despite the benefits to employees, managers and organisations in terms of facilitating continued remote work, we illuminate the hidden inequities within this work model and highlight the unintended consequences from the standpoint of gender, race and the digital divide. We identify key aspects of WFH that represent underlying factors which create conditions for inequities and illustrate these with a case study. Additionally, we analyse the role of technology as a platform for WFH, noting impacts on employee well-being, team dynamics, as well as manager-employee relations. We conclude by raising implications for managers, offering recommendations to rebalance the inequities identified, such as developing an inclusive organisational culture, creating systems to access and evaluate employee feedback as well as developing appropriate response mechanisms that support particularly vulnerable groups.

This paper analyzes previously unmeasured effects of a response to a service incident called “benevolent” within the customer –firm relationship.

A questionnaire was administered to telecommunication customers in a Western European country, and the model was estimated using partial least squares (PLS).

This study shows that the customer–firm relationship is surprisingly affected by the response to expected incidents that the customer interprets as acts of benevolence or opportunism. This research also shows that the firm's incident response interpreted as benevolence or opportunism has an effect that merely positive or negative events do not. Acts of benevolence response towards an incident positively affect customer–firm relationship quality, and expectations of such acts may lead to an upward spiral in customer commitment.

While benevolence trust has been proposed and studied before, the response to incidents interpreted as benevolent or opportunistic and their consequences have been under-studied, hence exhibiting a research gap.

在公司與顧客之間的關係的範疇內, 公司對服務事故所作的被稱為仁慈的反應的影響不曾被測量; 本研究擬就該反應的影響進行分析和探討。

研究以問卷方式進行¸ 對象為一個西歐國家裏的電訊服務客戶。研究人員使用偏最小平方法作估算, 設計有關的模型。

研究結果顯示、若顧客對公司就預期的事故所作的反應看作是仁慈的話, 又或相反地視為是投機主義所驅使的, 則公司與顧客之間的關係會出人意表地受到影響。研究結果亦顯示、顧客的仁慈、抑或是投機主義的闡釋和理解所帶來的影響, 與單純積極正面事件或消極負面事件所帶來的影響是不一樣的。公司對事件仁厚的回應會給予顧客之間的關係的質量帶來正面的影響; 而且, 對這種回應的預期、或會給客戶承諾帶來螺旋上升。

對仁厚之信賴曾被提出並作探討。唯對被視為仁慈的或機會主義作祟的反應所作的研究則不足, 這方面實存研究缺口。

This paper aims to showcase the trends in the research topics and their contributors over a time period of 30 years in the International Journal of Contemporary Hospitality Management (IJCHM). To be specific, this paper uncovers IJCHM’s latent topics and hidden patterns in published research and highlights the differences across three decades and before and after Social Sciences Citation indexing.

In total, 1,573 documents published over 199 issues of IJCHM were analyzed using two computational tools, i.e. metaknowledge and structural topic modeling (STM), as the basis of the mixed method. STM was used to discover the evolution of topics over time. Moreover, bibliometrics (and network analysis) were used to highlight IJCHM’s top researchers, top-cited references, the geographical networks of the researchers and differences in the collaborative networks.

The number of papers published continually increased over time with changes of key researchers publishing in IJCHM. The co-authorship networks have also changed and revealed an increasing diversity of authorship and collaborations among authors in different countries. Moreover, the variety of topics and the relative weight of each topic have also changed.

Based on the findings of this study, theoretical and practical implications for hospitality and tourism researchers are provided.

It is the first attempt to apply topic modeling to a leading academic journal in hospitality and tourism and explore the diversity in contemporary hospitality management research (topics and contributors) from 30 years of published research.

With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction.

In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting.

Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters.

This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction.

This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey techniques, the research measured social media sentiment to measure threats to switch.

The research involved a 12-month analysis of social media sentiment, specifically customer threats to switch banks (churn). These threats were then analysed for co-occurring themes to provide data on the reasons customers were making these threats. The study used over 1.7 million social media posts and focused on all five major South African retail banks (essentially the entire sector).

This study concluded that seven factors are most significant in understanding the underlying causes of churn. These are turnaround time, accusations of unethical behaviour, billing or payments, telephonic interactions, branches or stores, fraud or scams and unresponsiveness.

This study is unique in its measurement of unsolicited social media sentiment as opposed to most churn-related research that uses survey- or customer-data-based methods. In addition, this study observed the sentiment of customers from all major retail banks across 12 months. To date, no studies on retail bank churn theory have provided such an extensive perspective. The findings contribute to Susan Keaveney’s churn theory and provide a new measurement of switching threat through social media sentiment analysis.