Search results

In the modern financial landscape, Artificial Intelligence (AI) is gaining prominence, offering significant economic advantages. This research paper aims to investigate the impact of Behavioural Biases (BB) such as Overconfidence Bias (OCB), Fear of Missing Out (FOMO), Herding Bias (HB) and Regret Aversion Bias (RAB) on Investment Decision-Making (IDM). Additionally, it explores how the AI-led Adoption of Digital Advisory Services (ADAS) moderates these biases among Gen Z investors in India.

The study utilized a convenience sampling method, gathering 457 responses from Gen Z investors in India through an online survey questionnaire. The data was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM).

The results confirm a significant relationship between OCB, FOMO, HB and RAB on IDM. The study also found that ADAS significantly moderated the relationship between FOMO and IDM, as well as between HB and IDM. However, the moderation effect of ADAS was not supported for the relationships between OCB and IDM, and RAB and IDM.

This research offers valuable insights for academics, individual investors, fintech companies and policymakers. It highlights how behavioural biases affect IDM and underscores the importance of AI-enabled digital services in helping Gen Z investors recognize and manage these biases. Policymakers can use these insights to establish standards for AI use, ensuring regulatory compliance and promoting ethical conduct in AI-driven investment decisions.

The novelty of this study lies in its conceptual approach, particularly in examining the moderation role of ADAS in addressing behavioural biases among Gen Z investors.

This paper considers carbon abatement in a competitive supply chain that is composed of a manufacturer and two retailers under vertical shareholding. The authors emphasize the equilibrium decision problem of stakeholders under vertical shareholding and different power structures.

A game-theoretic approach was used to probe the influence of power structure and retailer competition on manufacturers' carbon abatement under vertical shareholding. The carbon abatement decisions, environmental imp4cacts (EIs) and social welfare (SW) of different scenarios under vertical shareholding are obtained.

The findings show that manufacturers are preferable to carbon abatement and capture optimal profits when shareholding is above a threshold under the retailer power equilibrium, but they may exert a worse negative impact on the environment. The dominant position of the held retailer is not always favorable to capturing the optimal SW and mitigating EIs. In addition, under the combined effect of competition level and shareholding, retailer power equilibrium scenarios are more favorable to improving SW and reducing EIs.

This paper inspects the combined influence of retailer competition and power structure on manufacturers' carbon abatement. Distinguishing from previous literature, the authors also consider the impact of vertical shareholding and consumer preferences. In addition, the authors analyze the SW and EIs in different scenarios.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

This study aims to explore the effects of expertise diversity on project efficiency and creativity in health-care project teams.

This study analyzes hierarchical linear models using multi-source data from 50 project teams in a large health-care organization in the USA. This data set includes self-reported survey responses from 274 team members and human resource information for all 515 members across the 50 teams. Expertise diversity is operationalized by professional diversity and positional diversity reflecting two dimensions, domain and level, of the concept of expertise.

This study reveals that professional diversity is negatively related to project efficiency and project creativity, whereas positional diversity is positively related to project efficiency.

Successfully managing a project team of experts within a limited time frame is a challenge for organizations. This study advances the understanding of the double-edged sword effect of expertise diversity on project teams, focusing on professional and positional diversity. It provides important insights for human resource development in terms of the composition of project teams regarding members’ expertise.

This study aims to investigate the corrosion behavior of Cu-containing 3Ni steel in simulated marine environments and to provide basic guidance for improving the corrosion resistance of marine high-strength steels.

The corrosion properties of Cu-containing 3Ni steel were evaluated in five different NaCl concentrations by alternating wet and dry cycling method. The corrosion behavior was investigated by electrochemical impedance spectroscopy, scanning electron microscopy, X-ray diffraction and X-ray photoelectron spectroscopy. The mechanism of the influence of Cl ion concentrations on the corrosion behavior of Cu-containing 3Ni steel in marine environments was analyzed.

The results showed that the corrosion resistance of Cu-containing 3Ni steel decreased with NaCl concentration increasing. With the increase of NaCl concentration, the number of FeOOH particles decreased and their size increased, resulting in an increase in the porosity and a decrease in the density of corrosion products. High NaCl concentration could inhibit the formation of NiFe₂O₄ and disrupt the electronegativity of the inner film of corrosion products, which further weakened the enrichment of Ni and Cu, and enhanced the permeability of Cl ions.

The influence of NaCl concentrations on the corrosion behavior of Cu-containing 3Ni steel was systematically studied and the influence laws of corrosion behavior were obtained in this paper, providing basic data for the optimal design of Cu-containing 3Ni steels.

We aim to investigate the impact of different story types on small fashion business brand trust and purchase intention, guided by signaling theory. We investigate two potential moderators – product aesthetic judgment (i.e. consumers’ responses to the aesthetic aspects of a product) and underdog positioning (i.e. brand’s positioning as a loser that is not a leader in the marketplace and has only a small market share but puts efforts into creating valuable products) – that may influence consumers’ responses to a story.

We developed video stimuli that tell business stories to empirically compare two story types bringing brand trust: identity-focused and product-focused stories. We conducted two experimental studies and tested six hypotheses with 302 datasets. Study 1 compared eco-friendly storytelling with product-focused storytelling, considering perceived product aesthetic judgment as the moderator. Study 2 repeated the experiment with a founder story and a product-focused story, considering the perceived underdog as the moderator.

The findings suggest that the influence of eco-friendly storytelling, compared to product-focused storytelling, on higher purchase intention is fully mediated by brand trust. Consumers’ perceived product aesthetic judgments toward an eco-friendly product can positively moderate the storytelling effect and brand trust. However, founder storytelling did not bring higher brand trust and, consequently, had no significant impact on purchase intention. Similarly, the perceived underdog did not moderate this relationship.

The study is novel as it is one of the first to compare story types based on story contents, whereas previous studies have focused on story delivery. Especially, we explored which story types and contents effectively build brand trust for small businesses, a critical factor for their success. Based on the extant literature, we categorized story types into identity-focused stories and product-focused stories based on contents. We hypothesized and concluded that an identity-focused story can be more effective in eliciting consumer responses. Furthermore, we confirm the critical role of brand trust as a mediator in bringing about purchase intention through eco-friendly storytelling.

Avatars, which are used as a technology and marketing tactic, can embody consumer-facing employees and mimic their real-life roles on companies' websites, thereby playing a key role in enhancing the relationships between consumers and brands in the online environment. Academics and practitioners have increasingly acknowledged the significance of the consumer-brand relationship in both traditional and online contexts. However, the impersonal nature of the online environment is considered to be a hindrance to the development of these relationships. Despite the importance of this technology, little attention has been paid to the investigation of the avatar concept from a marketing perspective. This paper explores the nature of the avatar concept, including its main characteristics, dimensions, and conditions as well as the attitudinal and behavioural consequences of avatar users.

Adopting the qualitative design, a taxonomy was developed from interviews. In total, 42 interviews were conducted with current university students. 30 participants participated in the exploratory interviews. A total of 12 interviews were conducted during the in-depth stage based on findings in the preceding research.

Based on the qualitative data analysis, a taxonomy was developed. The idea of the taxonomy is summarized in that different dimensions of the avatar are considered the main base (first phase) of the taxonomy. There are consequential three parts: the attitudinal consequences related to the website; the attitudinal consequences related to the brand; the behaviours towards the brand. These behaviours represent the final phase of the taxonomy.

By developing a taxonomy of using avatars on brands' websites, the authors advance the understanding consumer-brands relationships. Using avatars' verbal interactions helps in shaping consumers' cognitive, affective, attitudinal and behavioural responses and add vital empirical evidence to the increasing body of research and practices involving avatar usage in the interactive marketing area.

The different dimensions of the online engagement behaviors exhibited by omnichannel shoppers, who mainly rely on the online channel for information search, are still understudied. This study aims to investigate how service journey quality (SJQ) has an impact on the overall omnichannel customer experience leading to customer identification (CI) with the store, subsequently leading to their exhibition of online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions.

The research is cross-sectional, quantitative and descriptive. Purposive sampling was used to choose the research's participants. Data were collected from 591 Indian omnichannel customers who had previously made an omnichannel purchase that included the concurrent usage of various channels of a retailer using a verified self-administered survey. Using the Smart PLS 4.0 software, the proposed conceptual model has been evaluated.

The results indicate that omnichannel customer experience mediates the relationship between SJQ and CI with the store, subsequently leading to their exhibition of online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions). The perceived customer gratitude toward the store significantly and positively moderated the direct relationship between SJQ and different online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions).

The study relied upon the omnichannel shoppers of only Indian population and relied on a cross-sectional data collection procedure for this research.

Post-pandemic, with highly dynamic shifts in customer preferences, the need for channel-agnostic shopping leading to the unpredictability of purchase patterns has made SJQ the only dimension to achieve sustainable loyalty intentions through value co-creation in an omnichannel retail context. Emphasizing post-purchase behaviors like different online engagement behaviors (writing online reviews, blogging, rating products and services online and indulging in customer-to-customer online interactions), this study is the first to show that SJQ might affect four different online customer engagement behaviors through omnichannel shopping experience and CI with the store. The moderating effect of customer-perceived gratitude toward the retailer on a few proposed hypotheses was also tested to give managerial recommendations. The study also answers the call to investigate the moderating role of customer gratitude in determining service quality-driven engagement behaviors.

The literature suggests that increasing the intensity of compensation incentives for corporate venture capital (CVC) managers can contribute to successful exits of direct CVCs. This study explores the impact of compensation incentives on the successful exits of indirect CVCs under different geographical distances between parent companies and indirect CVC managers.

The authors observed the compensation terms of CVC managers through investment announcements made by listed companies and used a probit regression model to test the hypotheses from a sample of 241 investment events with indirect CVCs in China.

The results show that if parent companies are geographically close to the managers of indirect CVCs, increasing the intensity of compensation incentives for managers will help the successful exit of indirect CVCs. However, if parent companies are not geographically close to indirect CVC managers, increasing the intensity of compensation incentives for managers will not promote the successful exit of indirect CVCs.

This study contributes significantly to the CVC literature. First, it sharpens our understanding of the differences in operational mechanisms between direct and indirect CVCs. Second, we find that the threshold returns of indirect CVC managers are non-negligible compensation incentives. Finally, the empirical evidence supports that in indirect CVC investments, the geographical distance between parent companies and managers is concerning because it affects whether compensation incentives contribute to the successful exit of indirect CVCs.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.