Management Science & Industrial Management Technolgy

A longitudinal case study of a culture transformation program at the level of senior administration of a large high-tech corporation (LHT) is presented in this research. Organizational reform efforts have long been known to have unexpected repercussions, and the story at the LHT was no different. A tailored curriculum was created to help senior managers become better achievers, and circles of peer coaching were formed to help with the transition. We present how the peer coaching program departed from its stated aims, resulting in a very favorable culture of discussion and undefended conversations inside the coaching circles, based on our observations and interview data. We believe that the peer group's existing openness for change, as well as the fact that they had to meet with their official subordinates on a regular basis, resulted in the unexpected but beneficial consequences of this change endeavor.

Millions of Muslims visit Makkah Al-Mukarramah every year for Hajj and Umrah. It is a mandatory part of both Hajj and Umrah rituals to visit Al Masjid Al Haram for various activities. Huge crowds and lack of prominent wayfinding signs make Hajis spend more time inside Haram trying to find their way that too in a tense and panicky state of mind. This paper aims at assessing the gravity of wayfinding associated challenges faced by Hajis by applying the well-known Customer Satisfaction Model and proposing possible solutions to minimize potential adverse effects. Convenience sampling was used to collect the data from the proposed sample size of 2000 from various nationalities and regions. A total of 618 responses were received. The structural equation modeling (SEM) method was used for path analysis using AMOS 21 analytical tool and results revealed a reasonable fit between data collected and the model used: chi2 (485.95), chi2 / DF (3.77), RMSEA (0.07), CFI (0.92), and all values of Cronbach's alpha are greater than 0.78. The results substantiated that Hajis face problems in wayfinding inside Haram, which leads to Hajis finding it challenging to navigate in the Haram area. When the respondents were presented with alternative solutions to improve their wayfinding inside Haram, the results showed a statistically significant improvement in the satisfaction level.

There has been relatively little research done to determine the level of user adoption of augmented reality (AR) innovations in the hospitality and tourism industry, especially in the travel sector of Hajj and Umrah. This is despite the fact that augmented reality (AR) technologies are becoming increasingly important and popular in these industries. Guest perceptions and acceptance intention were investigated and evaluated using structural equation modeling. The research was based on a questionnaire that was distributed to 967 people who went to the Holy Mosque in Makkah (SEM). The technological acceptance model was utilized in order to investigate adoption (TAM). The results of the study showed that the data that was gathered and the model that was employed was able to reasonably fit together: chi2 (1844.91), chi2 / DF (2.5), RMSEA (0.13), CFI (0.75), as well as all scores of internal reliability alpha are greater than 0.70. The perceived value of augmented reality (AR) by Haram visitors, which is determined by the benefits of utility and simplicity of use, coupled with behavior and attitude towards the actual usage of AR, is a factor that contributes to the adoption of AR.

There are many calls in the literature to investigate the Blockchain technology adoption (BCT) in Canadian Organizations and its impact on boosting enterprises' competitive advantages. Although the literature requires more research cases, it is more timely and relevant that the analysis be done as early as today. Various empirical supports for Technology Acceptance Model (TAM) are available depending on situation specifics. TAM remains a widespread and convenient theoretical framework for examination of aspects contributing to technology acceptance. This study aims to find the driving forces that effectively illustrate the blockchain technology adoption in Canadian Pharmaceutical Organizations and to be able to face the challenges associated with the process of adoption. This study examined BCT application using contacts from Canadian Companies Capabilities directory (CCC) and applied SEM regression using AMOS software with 750 respondents from pharmaceutical businesses using TAM framework. Path analysis results were good: chi2 (4918.592), chi2 / DF (5.513), RMSEA (0.049), CFI (0.753), and TLI (0.804). Perceived ease of use, Perceived Usefulness, attitude towards use, and intention to use predicted BCT utilization, yet two relationships (i.e., PEOU->PU and PU->IU) were rejected in the tested model as they show negative conformity results. All components explain more than 50% of variation, hence presenting a reasonable fit between the data examined and the research model. These findings will help in understanding of pharmaceutical organizations' adoption of BCT for researchers, regulators and developers and providing supported evidence on factors contributing to the adoption of BCT in Canadian Organizations.

The observational study revolves around the attitudes towards entrepreneurship. A survey of 387 Canadian immigrants, predominantly from the South Asian countries, i.e., Pakistan and India, was conducted. The data was then analysed using SmartPLS 3.2.8. The study reveals that perceived structural support (PStS) is a weak pointer of entrepreneurial intentions. Instead, personal attributes, i.e., self-efficacy and internal locus of control, contribute to entrepreneurial intentions. The outcomes also raise questions about the Canadian government's immigration strategy, which chooses foreigners based on financial potential and not on starting their own business. The study serves as a guideline for policymakers that if immigrants carrying personal attributes of high self-efficacy provided social support in the immigrant country, they could quickly start a new business.

This study aimed to examine consumer perceptions of artificial intelligence (AI) by analyzing YouTube comments using topic modeling. A natural language processing model called BERTopic was employed to identify latent topics in a total of 93,729 Englishlanguage comments. These comments were posted on 20 popular AI-related videos, each of which had over 1 million views, between January and December 2021. The analysis of the comment data yielded five major themes, each with its corresponding percentage representation. The first theme, accounting for 27.2% of the comments, revolved around the fear of AI superiority, reflecting concerns about AI surpassing human control. The second theme, comprising 13.4% of the comments, focused on anthropomorphism and the uncanny valley effect, indicating mixed emotions towards human-like robots. The third theme, representing 31.7% of the comments, highlighted AI bias and hallucinations, shedding light on the impact of AI inaccuracies on trust. The fourth theme, accounting for 17.2% of the comments, addressed ethical and moral concerns, particularly concerning AI development and oversight. Lastly, the fifth theme, encompassing 10.5% of the comments, explored AI trustworthiness, emphasizing the reliability of AI in decision-making processes. Based on the findings, this study challenges previous research that primarily reported positive AI experiences. It quantitatively identifies complex negative emotions towards AI technologies across diverse user demographics. Furthermore, it expands our understanding of how anthropomorphism and AI "hallucinations" influence trust, which are critical issues as AI becomes increasingly integrated into society. The application of BERTopic enabled nuanced modeling of online discourse, bridging gaps in our knowledge regarding consumer perceptions of emerging technologies on a large scale. In terms of novelty, this study is the first to utilize BERTopic modeling to uncover consumer experiences with AI by analyzing social media comments on a large scale. The insights obtained from this approach offer compelling new perspectives compared to previous research in this field.

Objectives: This research aimed to identify the factors that influence the effective adoption of supply chain management systems, specifically focusing on customer satisfaction in the context of Saudi Arabia. Methods: Data was collected through convenience sampling, targeting a proposed sample size of 1000 individuals representing diverse nationalities and areas. A total of 629 valid responses were obtained. Path analysis was conducted using the Structural Equation Modeling (SEM) method and the AMOS 21 analytical tool. Findings: The results of the analysis showed a reasonably good fit between the model and the data, as indicated by the following statistics: Chi-square (2701.069), Chi-square / DF (5.204), RMSEA (0.042), PCFI (0.757), and TLI (0.848). The findings confirmed all hypothesized relationships, except for the relationships between customer expectations and customer satisfaction, as well as perceived quality and customer satisfaction, which lacked significant statistical associations. Additionally, the study revealed that customer expectations strongly predict perceived performance, perceived performance strongly predicts customer satisfaction, and customer satisfaction strongly predicts customer retention. Novelty: The research provides novel insights into the factors influencing the adoption of supply chain management systems and their impact on customer satisfaction and retention in the Saudi Arabian market. The study also highlights the relationships between customer expectations, perceived performance, customer satisfaction, and customer retention, contributing to the existing body of knowledge in this field. Implications: These findings have practical implications for decision-makers, offering guidance for enhancing the successful implementation of supply chain management systems. By improving customer satisfaction and retention, businesses can refine their product and service quality, ultimately leading to customer referrals and potential client acquisition. Overall, the study emphasizes the importance of adopting efficient supply chain management systems in achieving customer

Millions of Muslims visit Makkah Al-Mukarramah every year for Hajj and Umrah. It is a mandatory part of both Hajj and Umrah rituals to visit Al Masjid Al Haram for various activities. Huge crowds and lack of prominent wayfinding signs make Hajis spend more time inside Haram trying to find their way that too in a tense and panicky state of mind. This paper aims at assessing the gravity of wayfinding associated challenges faced by Hajis by applying the well-known Customer Satisfaction Model and proposing possible solutions to minimize potential adverse effects. Convenience sampling was used to collect the data from the proposed sample size of 2000 from various nationalities and regions. A total of 618 responses were received. The structural equation modeling (SEM) method was used for path analysis using AMOS 21 analytical tool and results revealed a reasonable fit between data collected and the model used: chi2 (485.95), chi2 / DF (3.77), RMSEA (0.07), CFI (0.92), and all values of Cr...

Cutting-edge technologies are leveraging businesses value chain in order to advance their competitive advantage, from this standpoint blockchain technology (BCT) gained a relative pace over the recent years demanding for more research cases. The present study aims to contribute in understanding the customers' acceptance of BCT in Saudi Arabia and tested through Technology Acceptance Model (TAM). Although empirical support for TAM varies depending on the context, it remains a popular and effective conceptual framework for analysing aspects that contribute to technological adoption or rejection. This study was validated with the response from 740 respondents involved with BCT adoption in companies listed in Tadawul (The Saudi Stock Market) in Saudi Arabia through the Structural Equation Modelling (SEM) regression technique using AMOS software. The path analysis results revealed that a reasonable fit for the model: chi2 (2476.851), chi2 / DF (3.057), RMSEA (0.054), CFI (0.829), and TLI (0.904). Four main factors (Perceived Ease of Use, Perceived Usefulness, Attitude Towards Use, and Intention to Use) were identified as a determinant of Actual use of BCT. All factors show average variance explained greater than 45%. The research findings help understanding User's adoption of BCT for researchers, regulators and developers and providing supported evidence on factors contributing to the adoption of BCT in Saudi Arabia. The study is restricted to firms based in Saudi Arabia. Future study will concentrate on various countries' BCT adoption to assess for the multi-group analysis impact on the model.

This study aimed to examine consumer perceptions of artificial intelligence (AI) by analyzing YouTube comments using topic modeling. A natural language processing model called BERTopic was employed to identify latent topics in a total of 93,729 Englishlanguage comments. These comments were posted on 20 popular AI-related videos, each of which had over 1 million views, between January and December 2021. The analysis of the comment data yielded five major themes, each with its corresponding percentage representation. The first theme, accounting for 27.2% of the comments, revolved around the fear of AI superiority, reflecting concerns about AI surpassing human control. The second theme, comprising 13.4% of the comments, focused on anthropomorphism and the uncanny valley effect, indicating mixed emotions towards human-like robots. The third theme, representing 31.7% of the comments, highlighted AI bias and hallucinations, shedding light on the impact of AI inaccuracies on trust. The fourth theme, accounting for 17.2% of the comments, addressed ethical and moral concerns, particularly concerning AI development and oversight. Lastly, the fifth theme, encompassing 10.5% of the comments, explored AI trustworthiness, emphasizing the reliability of AI in decision-making processes. Based on the findings, this study challenges previous research that primarily reported positive AI experiences. It quantitatively identifies complex negative emotions towards AI technologies across diverse user demographics. Furthermore, it expands our understanding of how anthropomorphism and AI "hallucinations" influence trust, which are critical issues as AI becomes increasingly integrated into society. The application of BERTopic enabled nuanced modeling of online discourse, bridging gaps in our knowledge regarding consumer perceptions of emerging technologies on a large scale. In terms of novelty, this study is the first to utilize BERTopic modeling to uncover consumer experiences with AI by analyzing social media comments on a large scale. The insights obtained from this approach offer compelling new perspectives compared to previous research in this field.

The observational study revolves around the attitudes towards entrepreneurship. A survey of 387 Canadian immigrants, predominantly from the South Asian countries, i.e., Pakistan and India, was conducted. The data was then analysed using SmartPLS 3.2.8. The study reveals that perceived structural support (PStS) is a weak pointer of entrepreneurial intentions. Instead, personal attributes, i.e., self-efficacy and internal locus of control, contribute to entrepreneurial intentions. The outcomes also raise questions about the Canadian government's immigration strategy, which chooses foreigners based on financial potential and not on starting their own business. The study serves as a guideline for policymakers that if immigrants carrying personal attributes of high self-efficacy provided social support in the immigrant country, they could quickly start a new business.