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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.

Ukraine hosts thousands of international students for educational tourism, of which more than 18,000 Indian medical students were compelled to escape Ukraine under emergency conditions of war. This paper aims to examine their intention to return to Ukraine to complete their education based on an integrated theory of planned behaviour (TPB) framework with added constructs, i.e. risk perception, career anxiety, rescue and relief memory.

The data were collected from 26 February 2022 to 30 June 2022 in two phases and two modes. It was ensured that the respondents were strictly confined to Indian medical students who had travelled to Ukraine for educational tourism. SPSS 25 and AMOS 23.0 were used to analyse the data. The hypotheses proposed were statistically tested.

The analysis reveals that the extended TPB model resulted in a strong model and the empirical findings corroborate that the students’ attitude, subjective norms, perceived behavioural control and career anxiety significantly and positively influence the students’ revisit intention (RI) while risk perception and rescue and relief memory have a negative influence on the RI.

The study provides timely insights and implications to the Ukrainian tourism industry, particularly educational tourism business and medical institutions under the present turmoil, which can also act as blueprint research for destinations with a similar unstable political background.

The primary value of this research work is that it provides an understanding of the intention of medical students (educational tourists) towards revisiting the war-hit destination of Ukraine.

This paper examines the relationship between behavioral biases, and demand decisions for life insurance products in Uganda.

Data were collected from 351 life insurance policyholders in Uganda. The authors used a cross-sectional survey by applying a structured questionnaire. Descriptive analysis was conducted and hypothesized relationships between the constructs were evaluated through the use of structural equation modeling.

Results indicate that, behavioral biases are significant predictors of life insurance demand among Ugandan policyholders. Also, the two behavioral bias variables (heuristic bias and prospect bias) are significant predictors of demand decisions for life insurance products.

These results are helpful for both insurers and regulators. For insurers, it is now evident that demand decisions for life insurance products are not fully rational. It is imperative for insurers to simplify life insurance product information (heuristics), integrate product education and widen dissemination of product information (prospect bias) to allow policyholders to come up with optimal demand decisions. While for insurance policymakers, the study provides an understanding of behavioral biases. With such insights, policymakers can identify exploitative and deceptive information that target policyholders to better guide life insurance documentation and product designs.

This study is the first to offer insights into behavioral biases' influence on demand decisions for life insurance products in a developing country like Uganda. By integrating prospects and expected utility theory, this study examines rationality and irrationality in demand decisions for life insurance products.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0201

This study examines the relationship between policyholders’ psychographic characteristics and demand decisions for life insurance products in Uganda.

The study is based on a cross-sectional survey. Using a purposive sampling method, 389 questionnaires were administered to life insurance policyholders in the four geographical regions of Uganda. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the primary data, specifically to test the relationships between the dependent and independent variables.

The findings indicate a positive and significant influence of psychographic characteristics on demand decisions for life insurance products. In addition, the analysis indicates that the two first-order constructs of psychographic characteristics, namely price consciousness and consumer innovativeness, are positive and significant predictors of demand decisions for life insurance products. In contrast, the third first-order construct religious salience, exhibits a negative and nonsignificant effect on demand decisions for life insurance products.

For insurance practitioners, to influence demand decisions, they should emphasize premium-related appeals in their marketing messages (price consciousness) ignore product decisions based on religious beliefs and norms (religious salience). They should also ensure that insurance products are highly trustable and experiential (consumer innovativeness). For insurance policymakers, it offers an in-depth understanding of customer psychographic characteristics, which can be used to identify exploitative information embedded in certain marketing campaigns targeting specific psychographic characteristics, for better regulation.

The study provides a basis for understanding lifestyle and personality characteristics (psychographics), which may influence demand decisions for life insurance products in a developing country like Uganda, where the insurance industry is at an early stage of development.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0440

The current study investigates the factors influencing graduates’ perceived employability by utilizing the stimulus-organism-response theory, in the post pandemic era.

A quantitative approach was employed to examine the hypotheses of the research framework through partial least squares structural equation modelling (PLS-SEM) on the SmartPLS software.

The result indicates that course structure has a positive effect on students’ grit and community of inquiry (CoI). Also, students’ grit and CoI have a positive relationship with students’ performance, while students’ performance has a positive relationship with perceived employability. Moreover, students’ grit, CoI and students’ performance sequentially mediated course structure and perceived employability, whereas readiness and self-directed learning strengthen the relationship between students’ performance and perceived employability.

The findings will benefit university management, government and potential employers on how confident the student is in the chances of a future career after graduating from a higher institution.

This study investigated the relationships among big data analytics capability (BDAC), low-cost advantage, differentiation advantage, market and operational performance underpinning the resource-based view (RBV) and the entanglement view of sociomaterialism (EVS) theories.

A total of 191 responses from members of the Federation of Malaysian Manufacturers were analysed using a structural equation modelling approach.

This study has conclusively demonstrated that BDAC is indeed a resource bundle comprising human skills, tangible and intangible resources. This study found that BDAC positively influences competitive advantage and firm performance. The differentiation advantage was found to be a key factor in explaining market performance. Theoretically, both RBV and EVS could be used to link BDAC, differentiation advantage and market performance to explain superior firm performance.

First, the sample is restricted to the manufacturers in Malaysia. Second, a single independent variable, BDAC, is used as a higher-order capability to influence competitive advantage, and thus, superior firm performance. Third, this study uses a self-reported survey, which means that only one respondent from each firm answered the questions. Fourth, this study excludes the focused strategy as it aims to investigate the competitive strategy used in the broader industry environment, rather than in a specific segment pursuing a focused strategy.

First, BDAC is a valuable, rare, inimitable and non-substitutable tool for manufacturers to enhance their firm performance. Second, BDAC is crucial for manufacturing firms to reduce costs and differentiate themselves. Third, a low-cost advantage may not help manufacturers achieve greater market and operational performance.

The relationship among BDAC, low-cost advantage, differentiation advantage, market and operational performance within manufacturing industry is empirically tested.

This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

This study aims to identify the essential elements impacting the adoption of blockchain technology (BCT) in supply chain management (SCM) by integrating the technology acceptance and information system success (ISS) models.

Questionnaire-based data was collected from 236 supply chain professionals from Beijing. The proposed research framework was evaluated using structural equation modeling (SEM) by using SPSS 23 and AMOS 24 software.

The empirical findings specify the positive influence of total quality on perceived usefulness and compatibility. Further, perceived ease of use positively influences perceived usefulness, compatibility and behavioral intention. Moreover, perceived usefulness positively impacts compatibility and behavioral intention. Compatibility positively influences behavioral intention. Finally, technology trust was found to be a significant moderator between perceived usefulness and behavioral intention and between perceived ease of use and adoption intention to use BCT in SCM.

This study empirically develops the second-order construct of total quality, representing the ISS model. Furthermore, this study established how the ISS and technology acceptance models influence behavioral intention through compatibility. Finally, this study confirmed the moderating role of technology trust among perceived ease of use, perceived usefulness and behavioral intention.

YouTube (YT) has become a trend among millennials, and thus, marketers are trying to harness the power of it to communicate with them. Global marketers need to understand the mechanism of communicating via YT advertising (YTAD), especially in India that consists of 440 million millennials to re-strategize their YT communications. Consequently, this study aims to examine the influence of YTAD on the cognitive attitude, namely, brand awareness (BA) and brand knowledge (BK) of Indian millennials. The study also tests the moderating impacts of gender, device used for YT watching (DEYTW), duration and frequency of YT watching (DUYTW and FEYTW) on BA and BK.

Generalized linear model – analysis of variance has been used to investigate the proposed relationships in the study. Responses of 294 Indian millennials who watch YTAD regularly have been used for the final analysis. Moderating effects were also tested using Bonferroni pairwise comparisons.

The results revealed that YTAD significantly improves the cognitive attitude (BA and BK). However, gender was not found to have any moderating effect in the relationship of YTAD with BA, whereas moderating effects of gender were observed in the relationship of YTAD with BK. Furthermore, other moderators including DEYTW, DUYTW and FEYTW were found to have significant moderating impacts in the above-mentioned relationships.

In a country like India, comprising the largest millennial population of the world who spends a significant portion of their time in watching YT, it becomes crucial for global marketers to understand how the cognitive attitude (i.e. BA and BK) of millennials improves by watching YT advertisements. As it will help them in strategizing their communications on YT to get favourable consumers’ responses like purchase intention and actual purchase that may happen only when people have favourable cognitive attitude towards advertised brands.

The study offers new perspective to the field of communication by investigating the impact of YTAD on the cognitive attitude (i.e. BA and BK) of Indian millennials.

Using dynamic capabilities (DCs) theory, the authors assess whether micro, small and medium-sized enterprises (MSMEs) can leverage DCs to improve operational capabilities (OCs) during the COVID-19 pandemic. The authors also identify whether organizational learning (OL) affects the relationship between DCs and OCs.

The authors test these propositions on a sample of 419 MSMEs from Australia and New Zealand.

DCs have no direct effect on OCs, technological or marketing capabilities (TCs or MCs). OL moderates the effect of DCs on both TCs and MCs.

The study assesses only MCs and TCs as OCs and does not explicitly measure pandemic impacts on organizations. However, the results illustrate the importance of OL during crises for recovery purposes.

Managers can use the findings to improve structure, processes and knowledge management emanating from MCs and TCs within organizations impacted by the COVID-19 pandemic.

The authors use a multi-dimensional measure of OL and show that during the pandemic, OL is a critical factor that allows organizations to transform the benefits conferred by DCs into MCs and TCs.