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The purpose of this paper is to explore the key role of WhatsApp as WhatsApp-customer relationship management (w-CRM) for strengthening the relationship in the education sector. The paper is exploratory in nature.

A self-administered questionnaire was developed with 20 items for four latent constructs. The data were collected from the undergraduate and postgraduate students of Sri Sri University, Cuttack (Odisha), India, based on convenience sampling. The hypotheses were tested through regression and correlation analyzes.

The findings of the study unveiled that the relationships between students and teaching staff, students and non-teaching staff, and teaching and non-teaching staff can be strengthened by the adoption of w-CRM.

WhatsApp can be an inevitable tool to emerge with traditional CRM system to facilitating the advance implementation of w-CRM in the service industry.

Although the present study is limited to one of the service sectors, i.e. education, it unfolds the scope of implementation of w-CRM in different services. Especially, small and medium enterprises can implement this tool hand to hand to maintain strong relationships with customers.

The present study aims to examine whether leisure time posts shared on WhatsApp status drive to travel and tourism consumption among users.

In this study, discriminant analysis was employed to test hypotheses and identify essential factors.

The study indicated that the eight most contributing factors are expressing happiness, planning leisure time, views and comments, attractiveness, inquiring about places, preferring to post, nice way of expression and relax. These factors came from the latent variables of attitude, motivation and self-expression. Overall, the main influencing factors are internal (attitude and motivation), followed by an external factor i.e. self-expression. Additionally, the findings indicated that these significantly and positively impact travel and tourism consumption.

The discriminators identified in the study would guide tour and travel agencies and the agencies' managers on how best to adopt WhatsApp and WhatsApp's status application to influence aspiring travelers.

This study enlarges the existing literature by integrating three factors, attitude, motivation and self-expression, into a model to influence the behavioral outcomes of aspirational travelers using WhatsApp status.

The current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.

A total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.

The study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.

The study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.

The study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.

This paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

The purpose of this paper is to identify the traditional practices in the modern banking system (MBS) and examine the effects of these on employee response, customer reactions and customer loyalty, in the context of public sector banks in India. The study also investigates the effects on customers of employees’ use of traditional banking practices in the MBS.

A total of 460 usable responses were gathered from customers of seven public sector banks in Bhopal (MP), India. The study scales were refined and validated by exploratory factor analysis and confirmatory factor analysis.

The results indicated that the MBS utilising traditional practices (MBSTP) significantly influences unfavourable employee responses, customer reactions and loyalty. In addition, employee responses in MBSTP motivate and generate unfavourable reactions of customers, which further influence their loyalty adversely towards public sector banks.

The identified traditional practices with MBS are expected to bring clarity to the issue of employee response, customer reaction and loyalty. This would help the management of banks.

The results of the analysis indicated that public sector banking services are facing the internal challenges by its own service processes and employees’ behavioural intentions.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

The purpose of this paper is to develop and test an integrated conceptual framework using construal level theory (CLT) to explain the differences in the effects of process and outcome service attributes on overall service evaluation and customer satisfaction based on consumption context (social), evaluation context (temporal) and individual characteristics (expertise).

The authors use two lab experiments (hotel and restaurant settings) and a field study of online reviews posted by actual hotel customers to test all the hypotheses.

Process (outcome) attributes have a stronger influence on service evaluations under low-level (high-level) construal. Specifically, process attributes have a stronger influence when customers are accompanied by proximal (vs distal) social group, evaluate a service under near (vs distant) temporal frame or have high (vs low) level of customer expertise.

Service managers can use the findings about the differences in the influence of social, temporal and individual variables on customer evaluations under process and outcome attributes to improve customers’ service experiences and satisfaction.

This paper extends past research on the influence of construal levels on customer decision making by exploring the differences in the effects of process vs outcome service attributes on overall service evaluation and customer satisfaction, under the influence of low (vs high) construal levels triggered by social, temporal and individual variables.

The purpose of this paper is to review and discuss the literature focusing on defining and measuring sentiments so as to understand their role in stock market behavior.

Critical review of the literature by analyzing myriad scholarly articles. The study is based on an analysis of 81 scholarly articles to critically analyze the approach toward defining and measuring market sentiments. The articles have been examined to identify and critique different classification of sentiment measures. A discussion is built to scrutinize the sentiment measures under the purview of theoretical underpinnings of the investor sentiment theory as well.

With more than five decades of research, the sentiment construct in finance literature is still ill-defined. Myriad empirical proxies of sentiment measures have led to conflicting results. The sentiment construct defined in financial theories needs to be revisited from the lens of sentiments defined in psychology.

The study is limited to analyzing the role of individual and institutional sentiments in equity markets. There is a need to explore sentiments with respect to different investment styles and strategies along with the type of investors.

Developing a suitable sentiment proxy can result in devising profitable trading strategies for investors. Understanding factors driving investor sentiments will help regulators to become more proactive and frame better policies.

This paper has leveraged psychology literature to highlight the limitations in development of sentiment construct in finance literature. By identifying stylized facts from reviewing the empirical literature, it highlights areas for future research.

This study aims to examine the effect of sustainability reporting on market value growth (MVG) of quoted companies in Nigeria. The corporate reporting system has evolved, and this study examined how it influences the perception of investors.

This study adopted an ex post facto research design with 167 listed firms as the population. A total of 28 quoted firms were chosen with the use of purposive sampling. Data from 2009 to 2018 were obtained from secondary sources. Content analysis was used as a tool to analyse the disclosures in sustainability reports. The model was estimated using pooled ordinary least square (multivariate regression). Company age and financial leverage were used as control variables.

This study found that the compliance level of the sampled firms with sustainability reporting requirements for the four dimensions are below average, and sustainability reporting does not have a significant effect on MVG with Prob. (F-stat) of 0.7212 > 0.05. Therefore, this study recommends that management should intensify efforts in ensuring maximum compliance with the sustainability reporting guideline of Global Reporting Initiative to reflect in their market value and ensure its growth.

To the best of the authors’ knowledge, this study is the original idea of the authors, although references were made to previous related study but it is a unique research work of its own. The work contained in this paper (in full and part) has not been previously submitted to any other journal for publication.

The purpose of this paper is to measure the impact of financial inclusion on rural development through cooperatives.

The primary data were collected from 540 beneficiaries of Cooperatives banks operating in three northern states of India, i.e., J&K, Himachal Pradesh and Punjab using purposive sampling during January to June 2016. Exploratory factor analysis, confirmatory factor analysis, ANOVA, t-test and structural equation modelling were used for scale purification and data analysis.

The findings of the study revealed that financial inclusion through cooperatives has direct and significant impact on rural development. Further, the results support the notion that financial inclusion is a strategy of inclusive growth, but inclusive growth itself is a subset of a larger set of inclusive development which means that the benefit must reach the all, particularly the women and the children, minority groups, the extremely poor and those pushed below the poverty line by natural and human-made disasters.

The research has certain inescapable limitations. First, the in-depth analysis of the study is restricted to three northern states of India only because of time and resource constraints. Second, the study is confined to the perception of financial inclusion beneficiaries only, which in future could be carried further on the perception of other stakeholders such as SHGs, banking correspondents, etc. Third, possibility of subjective interpretation in some cases cannot be ruled out.

The study makes contribution towards financial inclusion literature relating to sustainable rural development and fulfils the research gap to some extent by assessing the impact of financial inclusion on rural development through cooperatives.

To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch.

This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering.

Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors.

ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.