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The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making.

The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency.

The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests.

The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs.

The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making.

The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem.

This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

For undergraduate and graduate students in universities, social media are playing an important role in their study/research because a large amount of academic information has been accumulated on social media. Indeed, social media are complementing university libraries. Given that intelligent recommender systems have been widely implemented on social media, this paper aims to examine the adoption mechanism of intelligently recommended information by university students in their study/research.

Building upon the updated information system success model and herding theory, this study developed a research model to examine the determinants of recommended information adoption in mobile applications for social media. Data were collected through an online questionnaire and analyzed with partial least squares structural equation modelling.

The results suggest that herding belief is a valid second-order construct, comprising two first-order dimensions of imitating others and discounting their own information. Information quality, system quality and service quality directly impact satisfaction with the intelligent recommender system. Furthermore, satisfaction with the intelligent recommender system and herding belief directly impact recommended information adoption by university students in their study/research.

This study draws on the updated information system success model and incorporates herding belief as an extended component to investigate recommended information adoption, providing a new lens for understanding recommended information adoption by university students in their study/research.

This study aims to examine whether the adoption of the International Integrated Reporting Council’s Integrated Reporting Framework (IIRC Framework) influences the extent of forward-looking disclosures provided by reporters.

This study captures forward-looking disclosures of Australian and New Zealand-based reporters by analysing integrated and annual reports over a period of 10 years from 2010 to 2019 using a machine learning algorithm. This study uses signalling theory to frame the analysis.

This study finds that the adoption of the IIRC Framework has a significant positive impact on the extent of forward-looking disclosures provided by reporting entities. The primary evidence suggests that while listing status alone negatively influences the extent of forward-looking disclosures, the additional analysis reveals that the acceptance of the IIRC Framework by listed entities is positively associated with an increase in forward-looking information. These results remain valid when subjected to a variety of robustness (alternative variables and country fixed effect) and endogeneity (system generalised method of moments and entropy balancing estimations) tests.

The findings have practical implications as regulatory agencies (including stock exchanges and standard setters), seeking to promote greater forward-looking disclosures, may want to encourage the adoption of the IIRC Framework.

The IIRC’s Framework promotes greater forward-looking disclosures benefiting stakeholders who gain a better understanding of the reporters’ future risks and opportunities (including social, economic and environmental risks) and how these are being managed/addressed.

This study provides novel evidence by highlighting the role played by the IIRC Framework in promoting forward-looking disclosures.

In light of the repercussions of the COVID-19 pandemic, electronic auditing otherwise known as computer-assisted audit tools and techniques (CAATTs) has become inevitable to automate the auditing process worldwide. Accordingly, the purpose of this study is to examine the influence of technological, organizational and environmental (TOE) factors on public sector adoption of CAATTs in developing countries such as Jordan under the COVID-19 pandemic conditions.

This study used 136 usable responses from the managers of internal audit (IA) of the Jordanian public sector entities. The data collected were analyzed using partial least squares-structural equation modeling. The TOE framework has been used in this study to consider a wide set of TOE factors. Then, this study suggests a CAATTs adoption model that incorporates the related technology factors of the diffusion of innovation theory to environmental and organizational factors. Further, this study contributes to the TOE framework by addressing government regulations, audit bodies’ support and audit task complexity as environmental factors affecting CAATTs adoption in the context of the public sector.

The results revealed that for technological factors, only the compatibility affects CAATTs adoption by the IA departments. For organizational factors, organizational readiness, top management support, auditors’ information technology competency and entity size were found to be significant factors. From the environmental factors, both government regulation and audit task complexity influence the CAATTs adoption. Besides, entity size moderates the influence of top management support on the CAATTs adoption in the public sector.

The findings could highlight the significance of the CAATTs adoption in the public sector institutions (by internal auditors) post-COVID-19, taking into consideration the TOE framework’s factors. Also, the findings are significant for the decision-makers and regulators in declaring new legislation for the electronic IA profession in the Jordanian public sector.

It turns out that the CAATTs adoption in the public sector can definitely enhance their ability to achieve the role of IA in preserving public funds and restricting corrupt practices within the public sector.

To the best of the authors’ knowledge, this study is one of the first studies that address the professional audit agency support and audit task complexity as environmental factors, as well as the entity size as an organizational factor, that affect CAATTs adoption in the IA department of the public sector.

There are two main objectives in this study. First, we aim to develop a set of constructs for health task management support (HTMS) features to evaluate which health-related tasks are supported by mobile health application (mHealth app) functions. Second, drawing on innovation resistance theory (IRT), we examine the impacts of the newly developed HTMS dimensions on perceived usefulness, alongside other barrier factors contributing to technology anxiety.

Using a mixed-method research design, this research seeks to develop new measurement scales that reflect how mHealth apps support older adults’ health-related needs based on interviews. Subsequently, data were collected from older adults and exploratory factor analysis was used to confirm the validity of the new scales. Partial least squares structural equation modeling (PLS-SEM) was used to analyze survey data from 602 older adults.

The PLS-SEM results indicated that medical management task support, dietary task support, and exercise task support were positively associated with perceived usefulness, while perceived complexity and dispositional resistance to change were identified as antecedents of technology anxiety. Perceived usefulness and technology anxiety were found to positively and negatively influence adoption intention, respectively.

This study enriches the information systems literature by developing a multidimensional construct that delineates how older adults’ health-related needs can be supported by features of mHealth apps. Drawing on IRT, we complement the existing literature on resistance to innovation by systematically examining the impact of five types of barriers on technology anxiety.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

Traditionally, the banking and financial sectors have pioneered adoption of new technologies and business models. One important digital banking model that has proven its efficacy in recent times, is Digital-Only Banking (DOB) where consumers interact with their banks through digital channels only. Having detailed knowledge of what actually happens at the consumer level during the adoption of new digital models and technologies is paramount to the success of these technological initiatives. The present study aims to investigate DOB adoption behavior and possible barriers using a quantitative approach at the consumer level. A conceptual model is developed by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model, incorporating Trust (TR), Perceived Risk (PR) constructs and cultural moderators of Individualism (IDV) and Uncertainty Avoidance (UA).

For this study, an online survey instrument was created and administered in Iran. The research sample was selected through the application of purposive sampling. Data from 788 respondents were analyzed. The proposed model was tested using Partial Least Square.?.s Structural Equation Modeling (PLS-SEM).

The results show that DOB adoption is positively influenced by Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC), while PR negatively influences DOB adoption intention. Unexpectedly, the results indicate that TR has no significant impact on DOB usage intention. Additionally, this study demonstrates that with individuals having a low level of IDV, the relationship between PE and BI is stronger, and with individuals having a low level of UA, the impact of SI on BI is stronger. It also reveals that the impact of TR on BI is stronger in low individualistic cultures.

DOB providers should enhance support features of their services or provide facilities that make it simpler for users to accomplish online transactions. Here, in order to improve the UI/UX design of their apps, DOB product managers should carefully observe the technical guidelines of the operating systems of digital devices, such as the Human Interface Guidelines (HIG) for iOS and Material You for Android. Additionally, DOB providers should build partnerships with mega online retailers to provide hassle-free and easy to use payment solutions for consumers.

DOB, as a novel and business model, has been investigated in very few studies, especially regarding any which focus on its adoption. To fill this gap, this research investigates DOB adoption through a modified version of the UTAUT model. The findings of this study suggest that future research regarding DOB should consider sources of TR, types of non-adopters, and context. This study, as the first of its kind in DOB literature, also highlights the significant role played by cultural values in customer behavior regarding DOB adoption.

A new round of technological revolution is impacting various aspects of society. However, the importance of technology adoption in fostering firm innovation is underexplored. Therefore, this study aims to investigate whether robot adoption affects technological innovation and how human capital plays a role in this relationship in the era of circular economy.

Based on the robot adoption data from the International Federation of Robotics (IFR) and panel data of China's listed manufacturing firms from 2011 to 2020, this study uses regression models to test the impact of industrial robots on firm innovation and the mediating role of human capital.

The results demonstrate that the adoption of industrial robots can significantly promote high-quality innovation. Specifically, a one-unit increase in the number of robots per 100 employees is associated with a 13.52% increase in the number of invention patent applications in the following year. The mechanism tests show that industrial robots drive firm innovation by accumulating more highly educated workers and allocating more workers to R&D jobs. The findings are more significant for firms in industries with low market concentration, in labor-intensive industries and in regions with a shortage of high-end talent.

Due to data limitations, the sample of this study is limited to listed manufacturing firms, so the impact of industrial robots on promoting innovation may be underestimated. In addition, this study cannot observe the dynamic process of human capital management by firms after adopting robots.

The Chinese government should continue to promote the intelligent upgrading of the manufacturing industry and facilitate the promotion of robots in innovation. This implication can also be applied to developing countries that hope to learn from China's experience. In addition, this study emphasizes the role of human capital in the innovation-promoting process of robots. This highlights the importance of firms to strengthen employee education and training.

The adoption of industrial robots has profoundly influenced the production and lifestyle of human society. This study finds that the adoption of robots contributes to firm innovation, which helps people gain a deeper understanding of the positive impacts brought about by industrial intelligence.

By exploring the impact of industrial robots on firm innovation, this study offers crucial evidence at the firm level to comprehend the economic implications of robot adoption based on circular economy and human perspectives. Moreover, this study reveals that human capital is an important factor in how industrial robots affect firm innovation, providing an important complement to previous studies.

The present research aims to explore the drivers of generative artificial intelligence (GEN AI)-based innovation adoption in the hospitality industry in Jordan.

To address the research gap and achieve the research work objectives, the Technology-Organization-Environment (TOE) lens and the structural equation modeling (SEM) approach were employed to analyze the sample data collected (n = 221) from the hospitality industry.

The findings indicate that relative advantage, top management support, organizational readiness, organizational culture, competitive pressures, government regulations support and vendor support significantly influence the GEN-AI-based innovation adoption, while the technological complexity is negatively associated with GEN-AI-based innovation adoption. Furthermore, the results showed there is no significant effect of cost on GEN-AI-based innovation adoption.

The paper analyses the TOE framework in a new technological setting. The paper also provides information about how GEN-AI-based innovation adoption may influence hospitality industry performance. Overall, this article provides new insights into the literature concerning AI technologies and through the TOE lens.