Institute of Business Management

The current study aims to investigate the effect of Chatbots' conversational attributes-perceived incredibility, inaccuracy, and incompetence, on users' creepiness in the context of hospitality booking platforms. Further, the research examines the influence of users' creepiness on their willingness to interact and choice deferral. The study utilized purposive sampling to collect data from 340 UK-based respondents using an online crowdsourcing platform. The data was analyzed using partial least squares (PLS-SEM) using Smart PLS 4.0. The study targeted respondents who had experience using Chatbots, AI-powered systems or any other type of tourism technology for their tourism activities. The present findings offer unique theoretical and practical implications and extensively enrich the body of knowledge in human-computer interactions. These critical insights would be helpful for hospitality businesses using online booking platforms to redesign AI algorithms for their Chatbots to reduce users' creepiness and strengthen behavioral responses.

The omnipresence of global forces and rapid technological advancements is shaping the behavior of SMEs as they increasingly adopt intelligent manufacturing to achieve sustainable goals. Despite their efforts, manufacturing SMEs in China are facing complex challenges while striving to maintain sustainable performance and promote green, ambidextrous innovation. These stumbling blocks create further challenges due to resource paucity and other related barriers. Nevertheless, an academic exploration of the factors that influence green ambidextrous innovation is warranted, mainly through the prism of Chinese manufacturing SMEs. Therefore, the present study is about how SMEs can benefit from green knowledge capabilities in balancing the exploration and exploitation of ambidextrous green innovation. The analysis is further augmented by incorporating green entrepreneurial intention as a mediator and digital financial literacy as a moderator. The data was obtained from 211 manufacturing SMEs in China through an email survey and analyzed using PLS-SEM. Findings reveal that green knowledge capabilities improve the green entrepreneurial intentions of SMEs to pursue ambidextrous green innovation. Results also confirm the role of digital financial literacy in enhancing green entrepreneurial intentions and fostering ambidextrous green innovation. The relationship is stronger for firms with high digital financial literacy and weaker for those with low digital financial literacy. Findings confirm that green entrepreneurial intentions serve as a prerequisite for firms to adopt environmental management plans, reflecting the firm’s commitment to green initiatives, proactiveness, and risk-taking.

PurposeThe fast development of artificial intelligence (AI) and machine learning techniques can help develop precise and trustworthy forecasting models with precision to predict the volatile and irregular stock behavior in the market. The purpose of this paper is to develop a robust predictive model that has the potential to use AI in enhancing the accuracy of stock market profit assessment. This study evaluates the effectiveness of a long short-term memory (LSTM) model for predicting the stock market behavior by incorporating key economic factors.Design/methodology/approachThis study constructs an LSTM model that incorporates economic factors influencing stock market direction, including interest rates, inflation, GDP and unemployment rates. These indicators are selected based on their theoretical relevance and empirical significance. A multivariate LSTM model is developed and validated against traditional autoregressive integrated moving average (ARIMA) and machine learning-based random forest (RF) models. Model performance is evaluated using root mean squared error, mean absolute error and directional accuracy.FindingsThe experimental results show that the performance of LSTM model varies across four different cross-validation folds, with the fourth fold appearing to have better generalization performance than the first fold, as both the mean training (0.0450687) and validation (0.01293) values are lower in the fourth fold than in the previous folds. This study highlights that the recurring neural network-based LSTM model, when combined with a holistic approach provides accurate and interpretable predictions for stock market indices of highly volatile markets. The results demonstrate that the LSTM model significantly outperforms ARIMA and RF models in terms of predictive accuracy. The LSTM effectively captures nonlinear patterns in financial time series and shows strong generalization capabilities.Practical implicationsThis study suggests that AI-driven techniques can be adapted to predict stock behavior with precision. In addition, such models may be effective for other market indices displaying similar behavioral patterns, aiding stakeholders in making informed investment decisions.Originality/valueThis study contributes to the body of knowledge by proposing a practical AI-based stock market prediction model that incorporates economic factors directly impacting stock market movements. This integral approach helps develop more accurate prediction models, providing insight into various elements of the stock market. The inclusion of baseline model comparisons and detailed residual analysis enhances the methodological rigor and applicability of the approach.

Sustainable entrepreneurship (SE) has recently gained global attention due to heightened environmental concerns and institutional pressure specifically in energy-intensive industries like construction. In spite of its relevance, there is scarcity of evidence on such mechanisms through factors such as green supply chain practices, upcycling and innovation affect sustainable entrepreneurship in the context of China’s construction sector. Thus, the present study investigates these relationships in the presence of green leadership as a moderator. Drawing on integrated perspective of stakeholder theory and dynamic capability view, the study tested the model by using PLS methodology. The study gathered the primary data from construction industry employees using survey questionnaires. The results indicated that the Green SC practices, upcycling, and innovation positively associated with SE in the construction industry in China. The outcomes also exposed that green leadership significantly moderates Green SC practices, upcycling, innovation, and SE in the construction industry in China. The study guides the regulators in establishing regulations to improve the SE using effective Green SC practices, upcycling, and innovation adoption.

Despite the growing application of gamification in green consumption, limited research is available on how different layers of gamification design interact to affect user behavior. Addressing this gap, this study develops a theoretical model based on the SOR framework and examines the impact of gamification immersion and complexity on users' psychological states (i.e., enjoyment and emotional exhaustion) and subsequent green consumption behavior. Using data from 502 Ant Forest users, structural equation modeling shows that gamification immersion increases enjoyment and reduces emotional exhaustion, while gamification complexity exerts the opposite effects. Additionally, the task completion strategy mitigates the adverse effects of complexity but attenuates the positive effects of immersion. This study extends green consumption research by identifying dual psychological pathways through which gamification affects green consumption behavior. It also introduces an innovative task completion strategy as a boundary condition that mitigates the negative effects of complexity, offering refined guidance for gamification design.