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Importance of corporate social responsibility (CSR) in marketing domain is increasing immensely. The effect of CSR perception on the purchase intention differs on the basis of mediators and contexts. The objective of this study is to examine the consumer behaviour of young consumers. For this, impact of CSR perception on purchase intention, satisfaction and price fairness of Generation Z is studied.

Preliminary data analysis is run to check normality, skewness and common method bias. PLS-SEM is deployed to examine the relationships amongst the research variables. Sequential mediation through PLS bootstrapping helped in exploring new and exciting research results which are supported with literature.

The CSR perception of Generation Z does not have a direct effect on their purchase intention. Interestingly, satisfaction and price fairness fully mediate the relationship between CSR perception and purchase intentions separately, i.e. CSR perception of Generation Z influences purchase intention only through satisfaction and price fairness. Furthermore, satisfaction and price fairness are also found to sequentially mediate the relationship between CSR perception and purchase intentions.

The research will aid not only the fast-food industry but the industries that are looking to focus on what Generation Z consumers expect in emerging markets including India. Understanding consumer expectations out of CSR initiatives will help them to incorporate social considerations into their marketing strategies and increase their profitability. Generation Z is regarded as the most challenging consumer demographic to market due to their proclivity for conducting extensive research and comparison shopping before making a purchase decision. As a result, the companies that want to use CSR as a strategy may find it advantageous to investigate how marketing of their CSR initiatives will lead to competitive edge and influence purchase decisions of this generational cohort.

This study adds to the academic literature by developing and evaluating a research model for consumer responses of a very important generation cohort to CSR in an emerging economy setting. CSR activities alone may not be enough to improve purchase intention of Generation Z adults. Sequential mediation for Generation Z adults' relationship between CSR and price fairness flows through satisfaction and finally to purchase intention is interesting because it clearly establishes a link amongst belief, attitude and actions of the target audience under study in a meaningful way within the framework given by cognitive consistency theory.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

The aim of this research is to explore how religiosity (RG) could influence the Islamic apparel brand personality (IABP) dimensions, and to determine the degree to which IABP, attitude (ATT), subjective norms (SN) and purchase intention (PI) are influenced by RG. In addition, this research attempts to investigate the significant relationship between IABP and the components of the theory of planned behavior in the apparel industry in Egypt.

This research adopts a quantitative research method to provide insights relating to relationships between variables. The research data were collected through a conducted survey of Muslim females in Egypt. A convenience nonprobability sampling technique for data collection was used. To achieve the research purposes, confirmatory factor analyses, reliability and validity tests and structural equation modeling were adopted.

The research results show that RG has a positive significant relationship with ATT, SN and PI of Islamic apparel. Moreover, it was that only ATT has a positive significant influence over the PI of Islamic apparel unlike SN and Perceived behavioral control (PBC). Results also found that there is a positive relationship between IABP with ATT and SN. However, the relationship between RG and IABP was not statistically supported.

The research provides practical implications for brand managers, designers and producers in the Islamic apparel sector on how to increase PIs by extending IABP as well as for Egyptian policymakers. The practical implications include the possible approaches that stakeholders of Islamic apparel brands need to address while promoting, and this will influence marketing strategies in general and branding specifically.

This study extends our understanding of consumers’ Islamic apparel purchasing intentions using TPB to determine its rationale. Unlike other studies, this study operated RG and IABP to assess their influence on Islamic apparel PI in Egypt.

This study aims to explore consumption practices of new middle-class Indian women to explicate how they are challenging traditional social norms and redefining their identity through their consumption practices.

A total of 32 semi-structured and photo-elicitation interviews were conducted with new middle-class women between the age group of 23 and 40 years in India.

This study illustrates how the doing of consumption practices that involve creating, controlling, knowing and transforming is enabling new middle-class Indian women to undo gender disparities embedded in hegemonic patriarchal social order. Also, the study provides new insights into how class and symbolic capital intersect gender to redefine middle-class women’s feminine self.

This study specifically illustrates how new middle-class women are using consumption practices to uplift their position in household; bring about new modes of social interface; and identity expression and a reversal in gender roles.

The conflation of women’s independence with consumerism underlines the need for marketers to position consumer goods in a manner that strengthens women’s self and alleviates cultural perceptions of women as subordinate to men in the household. Indian market has considerable growth potential for publicly visible brands that affirm the elevated social status of women and allow them to effectively demonstrate their capital resources.

An under-researched consumer segment is explored by focusing particularly on the intersection of discourses of women’s individuality with that of their consumption practices. Additionally, pioneering use of photo-elicitation technique coupled with hermeneutic approach enabled to elicit effectively women’s reflections on their behaviours, values and motivations underlying their consumption practices.

The purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences.

The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses.

The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance.

This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.

The current approach to valuing customers is based on the notion of discounted profit generated by the customers over the lifetime of the relationship, also known as customer lifetime value (CLV). However, in the financial services industry, the customers who contribute the most to the profitability of a firm are also the riskiest customers. If the riskiness of a customer is not considered, firms will overestimate the true value of that customer. This paper proposes a methodology to adjust CLV for different types of risk factors and creates a comprehensive measure of risk-adjusted lifetime value (RALTV).

Using data from a major credit card company, we develop a measure of risk adjusted lifetime value (RALTV) that accounts for diverse types of customer risks. The model is estimated using Stochastic Frontier Analysis (SFA).

Major findings indicate that rewards cardholders and affinity cardholders tend to score higher within the RALTV framework than non-rewards cardholders and non-affinity cardholders, respectively. Among the four different modes of acquisition, the Internet generates the highest RALTV, followed by direct mail.

This paper not only controls for different types of consumer risks in the financial industry and creates a comprehensive risk-adjusted lifetime value (RALTV) model but also shows empirically the value of using RALTV over CLV for predicting future performance of a set of customers. Further, we investigate the impact of a firm’s acquisition and retention strategies on RALTV. The measure of risk-adjusted lifetime value is invaluable for managers in financial services.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.