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This research investigates the factors influencing consumers' intention to purchase e-deals from group buying websites, focussing on e-deal proneness, price consciousness and anticipatory regret.

Three studies (n = 539) were conducted using data collected from an online consumer panel and tested via structural equation modelling and PROCESS macro in SPSS.

The findings suggest that subjective norms, perceived behavioural control and attitudes positively influence consumers' e-deal purchase intention. Additionally, price consciousness amplifies the relationship between consumers' e-deal proneness and purchase intention, and price-conscious respondents are more likely to have the intention to buy e-deals when faced with some form of anticipatory regret.

Based on the research findings, practitioners are advised to prioritise social norms and entertainment value when promoting the attractiveness of e-deals, using strategies such as social media and influencer marketing. Brands should also emphasise the value of e-deals by showcasing comparative price savings and discounts to motivate consumers to buy.

This paper addresses an interesting and practical issue related to the effects of group buying websites, focussing on e-deal proneness, price consciousness and anticipatory regret.

In keeping with global developments rendering online shopping as an emerging trend among consumers, the present study extends the unified theory of use and acceptance of technology (UTAUT2) comprising the digital payment mode (DPM) as a new driver of online shopping and with the mediation of attitudes toward technology (ATTs) to gauge a better and deeper understanding of behavioral intention (BI).

This study used a survey instrument with snowball sampling from 600 consumers in northern India. Partial least squares structural equation modeling was used to find the association between drivers using UTUAT2, along with DPM and ATTs. The data were divided into a test group (20%) and validated through a training group (80%).

DPM was shown to be directly associated with BI. The mediation of ATTs was also validated through the model. The predictability of the model was 67.5% for the test group (20%) and 69.6% for the training group (80%). The results also indicated that facilitating conditions is a critical driver of BI.

This study enhances the understanding of the roles that DPM and ATTs play in BI during online shopping, suggesting that Indian managers need to adopt DPM as a support service to make online shopping a worthwhile experience.

China's stock market, which serves as an example of emerging markets, is steadily maturing in the context of globalization. In order to analyze the pricing mechanism of China's stock market, this paper builds a six-factor model to address the market features that are structurally efficient but not entirely efficient.

This study updates the Fama–French factor model's construction process to account for the unique features of China's stock market before creating a model that incorporates size, volume, value, profitability, and profit-income factors based on institutional investors' trading behavior and research preferences. The SWS three-tier sector stock index's monthly and quarterly data for the years 2016–2021 are used as samples for this study.

The results imply that China's stock market is structurally efficient and exhibits high levels of rationality in the region dominated by institutional investors. Specifically, big-size and high-volume portfolios that perform well in terms of liquidity can receive trading premiums. Growth-style sectors prevail at present, and investing in sectors with strong profitability and reliable financial reporting data can produce better returns.

The research on China's stock market can be extended to improve the understanding of the development process of similar emerging markets, thereby promoting their improvement. To enhance the development of emerging markets, the regulators should attach great importance to the role of local institutional investors in driving the market to maturity. It is crucial to adopt a structured approach to examine the market pricing mechanism throughout the middle stage of the transition from developing to mature markets.

This study offers a structured viewpoint on asset pricing in growing emerging markets by combining the multi-factor pricing model approach with behavioral finance theories.

Islamic home financing products with Murabaha contracts are widely favored among the Muslim community in Indonesia, given that the country has a population of over 230 million Muslims. To facilitate the development of products and enhance public interest, it is important for Islamic banking institutions to comprehend the elements that may impact the intents of Muslim communities in Indonesia when selecting Islamic home financing products with Murabaha contracts. The purpose of this study is to ascertain the many aspects that may have an impact on the decision-making process of Muslim communities in Indonesia when selecting Islamic home financing product that use Murabaha contracts.

The partial least square-structural equation modeling data processing techniques will be used to process and evaluate these components. The data used in this study was acquired by administering questionnaires to a sample of 298 Muslim communities, which were randomly selected from a pool of 301 possible customers of Islamic house finance in Indonesia.

The results of this research show that attitude, subjective norms and perceived behavioral control have positive influence on intention to choose an Islamic home financing scheme with Murabaha agreement, while price fairness of Islamic home financing and Islamic altruism have direct and indirect influence on intention to choose Islamic home financing.

Analyzing factors that affect intention to choose Islamic home financing product under Murabaha contract is essential. Future study is required to analyze other Islamic home financing products, such as istisna, ijarah muntahia bi tamlik and diminishing musharakah. This study only serves as a foundation for further investigations into conventional approaches to home financing in emerging nations. The areas can be expanded to be implemented in other countries.

It is anticipated that Islamic banks have the capacity to cultivate a favorable and constructive perception, hence fostering a positive disposition among the Muslim populace in Indonesia. Furthermore, it is essential for Islamic banks to guarantee that all stakeholders within the sharia-compliant institution, particularly the frontline staff, have enough expertise and understanding of the intricacies of Islamic home financing products including Murabaha contracts, which are intended for prospective customers. In the foreseeable future, it is anticipated that the Muslim population in Indonesia would exhibit a greater intention toward the use of Islamic home financing solutions that use Murabaha contracts, facilitated by the establishment of a conducive environment.

This research integrates the impacts of pricing fairness and Islamic charity as a modified model, alongside the theory of planned behavior model, to examine the influence of these factors on individuals’ intentions to use Islamic home financing in Indonesia.

The study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns.

The study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models.

This study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors.

The study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.

This study aims to outline the research field of initial public offerings (IPOs) pricing and performance by combining bibliometric analysis with a systematic literature review process.

The study uses over three decades of IPO publication records (1989–2020) from Scopus and Web of Science databases. An analysis of keyword co-occurrence and bibliometric coupling was used to gain insights into the evolution of IPO literature.

The study categorized the IPO research field into four primary clusters: IPO pricing and short-run behaviour, IPO performance and influence of intermediaries, venture capital financing and top management and political affiliations and litigation risks. The results offer a framework for delineating research advancements at different stages of IPOs and illustrate the growing interest of researchers in IPOs in recent years. The study identified future research potential in the areas of corporate governance, earning management and investor sentiments related to IPO performance. Similarly, the study highlighted the opportunity to test multiple theoretical frameworks on alternative investment platforms (SME IPO platforms) operating under distinct regulatory environments.

To the best of the authors’ knowledge, this paper represents the first instance of using both bibliometric and systematic review to quantitatively and qualitatively review the articles published in the area of IPO pricing and performance from 1989 to 2020.

The price clustering behavior is becoming a core part of the market efficiency theory especially with the development of trading strategies and the occurrence of major and unprecedented shocks which have led to severe inquiry regarding asset price dynamics and their distribution. However, research on emerging stock market is scant. The study contributes to the literature on price clustering by investigating an active emerging stock market, the Muscat stock market one of the Arabian Gulf Markets.

This research adopts the artificial intelligence technique and other statistical estimation procedure in understanding the price clustering patterns in Muscat stock market and their main determinants.

The findings reveal that stock prices are marked by clustering behavior as commonly highlighted in the previous studies. However, we found strong evidence of price preferences to cluster on numbers closer to zero than to one. We also show that the nature of firm’s activity matters for price clustering behavior. In addition, firms with traded bonds in Oman market experienced a substantial less stock price clustering than other firms. Clustered stock prices are more likely to have higher prices and higher volatility of price. Finally, clustering raised when the market became highly uncertain during the Covid-19 crisis especially for the financial firms.

This study provides novel results on price clustering literature especially for an active emerging market and during the Covid-19 pandemic crisis.

Online brand is becoming a popular and major gateway for consumers for booking various services specifically when they travel for several purposes. The present study aims to explore whether exposure to two separate yet similar modes of communication intervene consumer's brand trust and their subsequent loyalty intention toward the brand. The study further aims to investigate whether consumer's price consciousness has any influence on association between brand trust and brand loyalty in the process of decision -making.

The present study follows a different approach to data collection. The data have been retrieved from online brand (Oyo) page on Facebook through Google Form application. In all, 289 useable responses were retrieved from the travelers aged between 18 and 30. Structural equation modeling using SPSS 25.0 and Amos 26.0 has been applied to examine the effects of brand communication and online reviews on brand loyalty through brand trust.

Empirical evidence supports that even after having strong brand communication, online reviews play a crucial role in consumer's brand loyalty through brand trust. The study further reveals that price consciousness acts as a significant moderator in the relationship between consumer's brand trust and brand loyalty.

The current research contributes to the online brand and marketing knowledge by empirically showing the pertinence of consumer–brand relationship in an online brand context through a parsimonious model by examining how the two distinct mechanisms of communication influences consumer brand trust and loyalty intention.

The parsimonious framework of consumer–brand relationship adds to explicating the dual marketing challenges of communication and to draw a positive consumer response (i.e. consumer brand loyalty). The study attempts to examine the impact of two distinct yet identical modes of communication which facilitate shaping consumer brand trust that reinforce the strategic value of the circumstance and equips it with solid theoretical structure within an endeavor of the strategic significance of online brand managers.

The intention of the empirics is to contribute to the general understanding of investor responses to market price shocks. The authors review assumptions about investor behavior in response to price shocks and investigate alternative rebalancing heuristics.

The authors use market data over 40 years to define market shocks. Portfolio rebalancing implements constrained Markowitz mean-variance (MV) heuristics.

Momentum rebalancing in portfolio management outperforms contrarian rebalancing in the study interval. Sensitivity analysis by decade, sector constraints and proportion of security holdings bought or sold continue to support momentum rebalancing.

The results are consistent with under-responding to price shocks at consensus levels in financial markets. The theoretical background provides a basis for experimental lab studies of shocks of different magnitudes under conditions in which participants have information on the levels of other participants and a condition in which they can only observe their previous estimates.

Managing portfolios in the face of price disturbances of different magnitudes is informed by empirical studies and their implications for investor behavior.

This is the first study the authors can locate that uses market data with alternative rebalancing heuristics to estimate price returns from the respective heuristics over a time interval of 40 years. The authors support the results with sensitivity estimates and consider implications for the underlying agent heuristics in light of background studies.

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.

We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.

Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.

Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.

We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.