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This paper aims to measure outcome quality in banks in India and to make a comparative assessment of its influence on customer service quality perceptions. Though both functional quality (i.e. how service is delivered) and outcome quality (i.e. what is delivered) are important aspects of service quality, it is the functional quality which has primarily been the focus of past studies.

The data used in the study are based on a survey of bank customers located in Delhi and National Capital Region. Using the exploratory factor analysis and confirmatory factor analysis, validity and dimensionality of the multi-item functional and outcome quality scales used in the study were assessed. A structural model of relationships of functional and outcome quality with overall service quality was tested through use of the structural equation modeling (SEM) approach.

The study finds outcome quality as being a significant and major determinant of customer service quality perceptions in banks. Inclusion of outcome quality in the analysis is, moreover, found to be helpful in capturing more exhaustively the variations present in customer overall service quality perceptions.

Both the functional and outcome quality in the study have been measured through scales adapted from past studies. But the same have not been found able to fully capture variations in customer service quality perceptions. More psychometrically sound scales to measure functional and outcome quality are needed. Studies in both the developing and developed countries and additional service sectors are called for to increase the generalizability of the study findings. Furthermore, nomological validity of the outcome quality scale needs to be investigated by relating it with other anent constructs, such as customer satisfaction and their behavioral intentions.

Instead of simply remaining preoccupied with functional quality, i.e. process or how part of service delivery, bank management also needs to gauze customer outcome quality perceptions (i.e. what the customers think they are eventually getting out of their transactions with the service provider) and exercise due care to see that customers in fact are getting the core banking tasks performed for which they approach the banks in the first instance.

Present study is first of its kind in investigating role of outcome quality in banking services sector in the context of an emerging market like India. Use of SEM for analyzing both the measurement and structural models constitutes another noteworthy feature of the study.

Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The purpose of this paper is to develop an effective document retrieval method, which focuses on reducing the time needed for the navigator to evoke the whole document based on contents, themes and concepts of documents.

This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network (MB–FF based NN). Initially, the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency (TF–IDF) and holoentropy to find the keywords of the document. Then, cluster-based indexing is performed using MB–FF algorithm, and finally, by matching process with the modified Bhattacharya distance measure, the document retrieval is done. In MB–FF based NN, the weights in the NN are chosen using MB–FF algorithm.

The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769, recall value of 0.7957, F-measure of 0.8143 and accuracy of 0.7815, respectively.

The experimental results show that the proposed MB–FF based NN is useful to companies, which have a large workforce across the country.

Research in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context.

A fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study.

The findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria.

This survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market.

The study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large.

To the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.

As the move from the business incubator loomed, Abdul Khan had to decide where his business should relocate to. ACEES Group LLC, a small consulting firm, had grown from three friends working out of Abdul Khan’s house to a 20-person firm generating more than a million dollars in revenue within five years. This growth had necessitated the need for a larger and more prominent place. Although Abdul knew he did not want to renew the lease at the incubator, and he did not want to move his business too far from its current location, but the decision he had to make was whether ACEES Group should lease a commercial place or buy its own property. He was particularly torn because the real estate prices had fallen considerably, and were now on the mend and interest rates were still low.

The primary source of materials in the case was an interview with the owner (pseudo name: Abdul Khan). The owner wishes to remain anonymous. The financial statements of the firm produced in the case have been modified by a fixed factor so as to disguise the actual numbers but not materially alter the information in any fashion. Other secondary sources of materials include information about the business incubator program, the MBE certification and its benefits through the State of Florida, real estate and lease rates in Central Florida and other economic information.

This case is primarily intended for undergraduate students taking a course in entrepreneurship, real estate investments or financial management, with emphasis on real estate valuation, cash flow forecasting and/or valuation of business. Students should be familiar with time value of money concepts, understand the concept of NPV and IRR, and preferably be comfortable in the use of Excel. This instructor manual provides all calculations of space needs analysis, and discounted cash flow analysis for lease vs buy analysis. A few suggestions to discuss qualitative aspects of this decision making are also included.

Investment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.

The current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.

Information availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.

Only active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.

This research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.

To the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

The purpose of this paper is to develop two meshfree algorithms based on multiquadric radial basis functions (RBFs) and differential quadrature (DQ) technique for numerical simulation and to capture the shocks behavior of Burgers’ type problems.

The algorithms convert the problems into a system of ordinary differential equations which are solved by the Runge–Kutta method.

Two meshfree algorithms are developed and their stability is discussed. Numerical experiment is done to check the efficiency of the algorithms, and some shock behaviors of the problems are presented. The proposed algorithms are found to be accurate, simple and fast.

The present algorithms LRBF-DQM and GRBF-DQM are based on radial basis functions, which are new for Burgers’ type problems. It is concluded from the numerical experiments that LRBF-DQM is better than GRBF-DQM. The algorithms give better results than available literature.

The primary objective of the study is to discover the most prominent criteria and sub-criteria among environmental issues, social dimensions and corporate governance factors that may impact individual equity investors' investment decisions.

The present study collected data from 438 individual equity investors from the North Indian region. To achieve the objectives of the study, a fuzzy analytic hierarchy process (Fuzzy AHP) was applied. The key considerations of the study were environmental, social and governance (ESG) factors.

The governance criterion was discovered to be the most significant factor influencing individual equity investors' investment decisions among the three ESG factors, followed by environmental criteria, while social criteria were shown to be the least influential.

The present study solely looked at ESG issues as drivers of stock investors' investment decisions. In the current world, however, many other factors, including behavioral biases, accounting information, ownership structure and fundamental analysis, can have a substantial influence on investors' investment decisions.

The study's findings widen the theoretical contribution in the field of responsible investment by asserting how ESG factors influence investors' investment decisions in the equity market. From a practical standpoint, this study applies to retail and institutional investors, portfolio managers, financial advisors, market regulators, corporations and society at large.

To the best of authors knowledge, no attempt has been made to prioritize the ESG issues that impact the investment decisions of individual equity investors. Ergo, this study contributes to the existing literature on socially responsible investment.

This paper aims to epistemologically extend and explore the present theories from prior research conducted in the area of responsiveness. Furthermore, it determines to benchmark the prominent theories, characteristics, context and methodologies (TCCM) used in the domain since its inception to advance the science and practice of marketing and logistics discipline.

A seven-step methodology (SSM) has been introduced to create a comprehensive dataset. Based upon the selection criteria of high-ranked journals and language, the research studies have been retrieved from Scopus, Web of Science, Business Source Complete and journal homepage to avoid the error of exclusion. Moreover, the dataset has been compiled using manual and electronic searches without any limitation of time.

The search for a suitable dataset retrieved 642 documents by identifying “1969” as the beginning year of research in the subject domain. The analysis found that responsiveness has been prominently studied in the manufacturing industry. The results also advocate responsiveness as the vital antecedent to performance and satisfaction. Frameworks have been proposed with significant propositions for future empirical testing and theory inventiveness by researchers.

The study pioneers its utility for retailers to recognize the firms' inherent abilities and strengths, which can be promoted to create responsiveness more than ever. The analysis results can act as the compelling force to understand the driving power of various factors influencing responsiveness.

Research in the area of behavioral finance has demonstrated that investors exhibit irrational behavior while making investment decisions. Investor behavior usually deviates from logic and reason, and consequently, investors exhibit various behavioral biases which impact their investment decisions. The purpose of this paper is to rank the behavioral biases influencing the investment decision making of individual equity investors from the state of Punjab, India. This research would provide valuable insight into the different behavioral biases to investors and other participants of the capital market and help them in improving investment decisions.

The research is conducted on the individual equity investors of Punjab, India. Fuzzy analytic hierarchy process was applied to rank the factors influencing the decision making of individual equity investors of Punjab. The primary factors considered for the study are overconfidence bias, representative bias, anchoring bias, availability bias, regret aversion bias, loss aversion bias, mental accounting bias and herding bias.

The three most influential criteria were herding bias, loss aversion bias and overconfidence bias. The five most influential sub-criteria were “I readily sell shares that have increased in value (C61),” “News about the company (Newspapers, TV and magazines) affects my investment decision (C84),” “I invest each element of my investment portfolio separately (C71)” and “I usually hold loosing stock for long time, expecting trend reversal (C52).”

Although sample survey conducted in the present study was based on a limited sample selected from a particular area that truly represented the total population, it is considered as the limitation of this study.

The outcome of this research provides investors with a better understanding of behavioral biases that influence their decision making. This study provides them a guideline on different behavioral biases that they should consider while making investment decisions.

The research model is based on the available literature on behavioral finance and the research results and findings would add value to the existing knowledge base.