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This study anchoring on the theory of conservation of resources examines the relationship between family incivility, negative rumination and service delivery. This study also analyses the mediating role of negative rumination in the relationship between family incivility and service delivery. This study also examines psychological capital and perceived organisational support (POS) as boundary conditions influencing the relationships postulated in the study.

To test the relationships among the variables, responses were drawn from 419 frontline hotel employees at two-time points and the hypothesised relationships were tested using structural equation modelling.

The structural equation modelling analysis proved that family incivility is negatively related to service delivery. It was also found that family incivility is positively associated with negative rumination and an inverse relationship between negative rumination and service delivery. This study also found support for the mediating role of negative rumination and the moderating roles of psychological capital and perceived organisational support.

This study findings extend the theory and provide guidelines for managers to mitigate the adverse effect of family incivility on employees and their service delivery. Employees and managers can use psychological capital and POS as strategies to prevent the spill-over effect of family incivility on service delivery.

To the best of the authors’ knowledge, no prior studies have examined the effect of family incivility on service delivery. By establishing the mediating mechanism and boundary conditions, this study adds value to the theory and practice.

There has recently been much interest in analysing the creation of personalised tourism services and studying their effect on organisations. However, there still needs to be more work in analysing their effect on happiness and the role that emotions play in these processes. This paper aims to analyse, in the context of personalised and innovative tourism services, which factors can encourage and improve managers' happiness.

A model of analysis is presented with five variables: tourism managers' happiness (TMH), innovative personalised tourism services, internal factors: emotions (IFE), organisational factors (OF) and personal factors (PF). Eight hypotheses are proposed and tested with a structural equation model.

The results allow the authors to affirm that personalised technological advances applied to tourism services not only contribute to improving the happiness of hotel managers but also in generating emotions that contribute to improving their attitude towards the company.

This scientific work has some limitations. Firstly, this study was carried out exclusively in Spain due to the relevance of this country in the international tourism sector, according to the World Tourism Organisation. The results achieved in this research should be contrasted with other studies in other territories. Secondly, the interviews and surveys were carried out at specific time intervals. It has not led to problems of significant bias in the variance of the standard method. Therefore, it is desirable to undertake longitudinal or cross-sectional studies for future research. Thirdly, it is interesting to develop theoretical models that include other psycho-directive or leadership style constructs to determine whether they holistically enhance the subjective well-being of hospitality managers. Moreover, other types of factors of a social or strategic nature can be considered, which can positively or negatively impact the analysed variables. Finally, future research can deepen the empirical analysis of the relationship between managerial competencies and digital innovation from the perspective of happiness management. These findings would contribute to a greater cognitive understanding of the implications of personalised and innovative tourism services on hotel establishments' happiness and economic benefits.

This paper shows the fundamental role of a happy leadership style in creating responsible, green and innovative environments in today's digital society. Furthermore, the happiness of tourism managers can contribute to the generation of high quality and excellent services that are in line with the principles of sustainable development.

Personalised technological advances applied to tourism services not only contribute to improving the happiness of hotel managers but also to generating emotions that contribute to improving their attitude towards the company. On the other hand, it has been observed that personalised and innovative tourism services generate positive effects at organisational, internal and personal levels. The following reflections are advanced: The development of internal factors such as the emotions of awe and gratitude or the generation of trust can enhance the happiness of tourism managers. The happiness of tourism business managers can be enhanced by developing OF such as smart-personalised tourism services and data protection. The happiness of tourism managers can be enhanced by the development of PF such as travellers' desires, expectations and needs, or other factors such as disposable income, health status or family situation.

This is the first empirical study that focuses on investigating how personalised and innovative tourism services affect managing happiness.

The present study investigates the relationship between high-performance work systems (HPWS) and employee flourishing-at-work (FAW) through psychological capital (PsyCap) and employee work passion (EWP).

A three-path serial mediation model was constructed to explore the proposed relationships. The hypotheses were empirically tested using SPSS 25 and AMOS 21.0. The study was conducted in India and included a total of 357 hotel employees as participants.

The study revealed that PsyCap and EWP were significant independent mediators between HPWS and FAW. Moreover, PsyCap and EWP were identified as serial mediators in the relationship between HPWS and FAW. Additionally, a direct relationship between HPWS and FAW was also found.

Implementing HPWS in hospitality organizations can foster PsyCap of the employees and foster EWP among their workforce. This approach not only increases the organization's competitive advantage but also enhances employees' holistic well-being, i.e. FAW.

This study marks a pioneering effort to establish a distinct relationship between HPWS and FAW. Moreover, the study introduces an innovative approach by employing EWP to delve into the influence of both HPWS and PsyCap on FAW.

The purpose of this study is to investigate the negative spillover effects of family incivility on employees’ ability to meet their work-related deadlines. Furthermore, we examine emotional exhaustion as a mediator and emotional intelligence as a moderator in this process.

We test our predictions through a multisource, time-separated data collection involving 161 employee-supervisor dyads working in various hospitality sector organizations in Pakistan – a relatively understudied context where the hospitality industry is struggling.

The findings of our study indicate that exposure to rude family behavior hinders employees’ ability to complete work tasks on time, as mediated by their emotional exhaustion. However, this harmful process can be curtailed if employees can rely on their emotional intelligence to regulate their own emotions and those of their family members.

The findings of this study provide pertinent insights for HR managers and organizations to help their employees deal with family issues. Moreover, organizations can benefit from introducing training programs that improve employees’ emotional intelligence and increase the likelihood that they do not default on their work deadlines.

This study extends the limited existing family incivility research by investigating its spillover effects on employees’ ability to meet work deadlines, a previously unexplored relationship. In addition, we highlight the roles of two pertinent aspects: emotional intelligence and emotional exhaustion.

The future of management studies is invariably steering towards online and hybrid modes of course delivery. Therefore, assessing the effectiveness of online course delivery is exceptionally crucial. This study attempts to evaluate the effectiveness of online course delivery in management education involving the instructor, participant and technological component. This study contributes to the body of knowledge in three ways. First, the study proposes an approach to assess the effectiveness of online courses in management education. The study demonstrates this by taking a case study of a business school (B-school) in southern India. Second, the study identifies the shortcomings and areas that need improvement to enhance the overall effectiveness further. Third, the study outlines suggestive measures to improve the effectiveness of online course delivery by addressing technical, infrastructural, instructor and student behavioral components.

To accomplish the objectives, a case study approach has been adopted and fuzzy logic has been used as a methodology to assess the effectiveness of online course delivery in management education.

The findings suggest that instructors' use of cases and animation during online sessions, use of whiteboards, digital pens and other tools, attempts to draw participant's interest and the users' sense of belongingness in the online cohort, self-discipline and motivation from students' side, easy to use Learning Management System (LMS), audio-visual platforms, active electronic communication and training on the technical aspect of the online platform need to be improved to enhance the effectiveness of online course delivery further. The current effectiveness of online course delivery in the case of B-school was found to be “Fair,” which is average in relation to the effectiveness labels.

This study doesn't investigate the factors that moderate the effectiveness of online course delivery and how the factors influence each other. Future research endeavors can be extended in this direction to enrich the body of knowledge with new insights. Apart from this, the results outlined in this study are about the status quo of the case B-school and can't be generalized. However, the methodology and approach can be adopted by other B-schools or higher educational institutes to measure the schools' and institutes' current level of effectiveness in online teaching.

So far, only a few studies have paid attention to the empirical assessment of the effectiveness of online course delivery consisting of engagement from the technical, instructor and participants' dimensions. This study proposes a novel approach to measure the level of effectiveness and identifies the shortfalls that impede good effectiveness in online course delivery.

Portfolio optimization is the process of making an investment decision on a set of assets to realize high returns with low risk. It has three major stages: asset selection, asset weighting and asset management. Asset selection is an important phase because it influences asset allocation and ultimately affects the returns of a portfolio. Today, there is an increase in the number of listings on a stock exchange. Therefore, it is important for an investor to screen and select stocks for investment. This study focuses on the first stage of the portfolio optimization problem, namely, asset selection. The purpose of this study is to evaluate and select profitable stocks quoted on National Stock Exchange (NSE) for portfolio optimization.

Financial ratios are considered as the input and output parameters for evaluating the financial performance of the firms. This study adopts a hybrid principal component analysis (PCA) and data envelopment analysis (DEA) approach to evaluate the efficiency of the firms. Based on the efficiency scores, the firms are selected for the investment process.

The model helps to determine the relative efficiencies of the firms. The efficient firms are considered to be the potential stocks for investment. It helps the investors to screen the stocks from a large number of stocks quoted on NSE.

One of the limitations of the standard DEA model is that it fails to discriminate the firms when the number of input and output parameters are larger than the number of firms. To overcome this problem, either a parameter can be ignored or weight-restricted DEA can be applied. When an input/output parameter is dropped, the information in that variable is lost. Weight-restricted DEA model uses expert opinion for measuring the relative importance of input and output parameters. Expert opinion is subjective and might be biased. The PCA-DEA model helps to identify the efficient firms by improving the discriminatory power of standard DEA without any loss of information and without the need for expert opinion, which might be biased.

Asset selection is an important stage in the investment process. Selection of stocks based on the efficiency score is an easier option available to the investors. But the misclassification of firms either due to biased expert opinion or discrimination inability of DEA can be costly to an investor. The PCA-DEA model overcomes both these limitations. Investors can select the potential candidates for asset allocation based on the efficiency scores obtained using the PCA-DEA model. Further, the relative efficiencies obtained can help the firms to benchmark their performance against the best performing firms within their industry.

This paper is one of few papers to adopt the PCA-DEA framework to select stocks in the Indian stock market.

This study aims to treat the development and application of sequence characterised amplified region (SCAR) markers for the detection of plant based adulterants (dried red beet pulp and powdered Ziziphus nummularia fruits) in traded ground chilli.

Adulterant‐specific DNA fragments (red beet pulp specific – “Beet 01” and Z. nummularia specific – “Ziz 01”) identified by random amplified polymorphic DNA polymerase chain reaction (RAPD‐PCR) analysis were cloned and sequenced for SCAR marker development. Red beet pulp specific SCAR primer pair, B1, and Z. nummularia specific SCAR primer pair, Z1, were designed from the corresponding RAPD marker sequences to amplify SCAR markers of 320 bp and 389 bp, respectively. The utility of the SCAR markers for adulterant detection was verified in model blends of chilli powder with the adulterants. Six commercial samples of ground chilli powder were analysed using the SCAR markers.

SCAR markers could detect the adulterants at a concentration as low as 10 g adulterant kg⁻¹ of blended sample. The Z. nummularia SCAR marker could detect the presence of Z. nummularia fruit adulteration in one of the commercial samples. All the market samples tested were free from red beet pulp adulteration.

The PCR‐based method developed in the study is simple, rapid, and sensitive and has the potential to be developed into a quantitative analytical method and for commercial PCR kits for the large‐scale screening of ground chilli to detect and prevent plant‐based adulterants. The work has public health significance too, as ground chilli is one of the major spices consumed worldwide.

The study is the first report on the development of SCAR markers for adulterant detection in ground chilli. This work has relevance, as adulteration is a major concern of the sanitary and phytosanitary issues of the World Trade Organization (WTO) agreement.

The purpose of this paper is to understand the customer sentiment towards telemedicine apps and also to apply machine learning algorithms to analyse the sentiments in the adoption during the COVID-19 pandemic.

Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like support vector machine (SVM) and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix.

The paper explores the customer sentiment towards telemedicine apps and their adoption during the COVID-19 pandemic. Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like SVM and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix. The customers who used telemedicine apps have positive sentiment as well as negative sentiment towards the telemedicine apps. Some of the customers have concerns about the medicines delivered, their delivery time, the quality of service and other technical difficulties. Even a small percentage of doctors feel uncomfortable in online consultation through the application.

The primary value of this paper lies in providing an overview of the customers’ approach towards the telemedicine apps, especially during the COVID-19 pandemic.

Research questions that this paper attempts to answer are – do the features in general email communication have any significance to a teaching faculty member leaving the business school? Do the sentiments expressed in email communication have any significance to a teaching faculty member leaving the business school? Do the stages mentioned in the transtheoretical model have any relevance to the email behaviour of an individual when he or she goes through the decision process leading to the decision to quit? The purpose of this paper is to study email patterns and use predictive analytics to correlate with the real-world situation of leaving the business school.

The email repository (2010–2017) of 126 teaching faculty members who were associated with a business school as full-time faculty members is the data set that was used for the research. Of the 126 teaching faculty members, 42 had left the business school during this time frame. Correlation analysis, word count analysis and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand the sentiment and its association in leaving the business school. From the email repository, a rich feature set of data was extracted for correlation analysis to discover the features which had strong correlation with the faculty member leaving the business school. The research also used data-logging tools to extract aggregated statistics for word frequency counts and sentiment features.

Those faculty members who decide to leave are involved more in external communication and less in internal communications. Also, those who decide to leave initiate fewer email conversations and opt to forward emails to colleagues. Correlation analysis shows that negative sentiment goes down, as faculty members leave the organisation and this is in contrary to the existing review of literature. The research also shows that the triggering point or the intention to leave is positively correlated to the downward swing of the emotional valence (positive sentiment). A number of email features have shown change in patterns which are correlated to a faculty member quitting the business school.

Faculty members of only one business school have been considered and this is primary due to cost, privacy and complexities involved in procuring and handling the data. Also, the reasons for exhibiting the sentiments and their root cause have not been studied. Also the designation, roles and responsibilities of faculty members have not been taken into consideration.

Business schools all over India always have a challenge to recruit good faculty members who can take up research activities, teach and also shoulder administrative responsibilities. Retaining faculty members and keeping attrition levels low will help business schools to maintain the standards of excellence that they aspire. This research is immensely useful for business school, which can use email analytics in predicting the intention of the faculty members leaving their business school.

Although past studies have studied attrition, this study uses predictive analytics and maps it to the intention to quit. This study helps business schools to predict the chance of faculty members leaving the business school which is of immense value, as appropriate measures can be taken to retain and restrict attrition.

Climate change is a massive multidimensional shift. Temperature shifts, in particular, have important implications for urbanization, agriculture, health, productivity, and poverty, among other things. While much research has documented rising mean temperature levels, the authors also examine range-based measures of daily temperature volatility. Specifically, using data for select US cities over the past half-century, the authors compare the evolving time series dynamics of the average daily temperature (AVG) and the diurnal temperature range (DTR; the difference between the daily maximum and minimum temperatures). The authors characterize trend and seasonality in these two series using linear models with time-varying coefficients. These straightforward yet flexible approximations provide evidence of evolving DTR seasonality and stable AVG seasonality.