Journal of High Technology Management Research最新文献

{"title":"AI enabled value-oriented collaborative learning: Centre for innovative education","authors":"J. Ramadevi ,&nbsp;C. Sushama ,&nbsp;K. Balaji ,&nbsp;Vamsidhar Talasila ,&nbsp;Nidhi Sindhwani ,&nbsp;Mukti","doi":"10.1016/j.hitech.2023.100478","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100478","url":null,"abstract":"<div><p>Collaborative learning allows students to pool their knowledge, skills, and experiences to better understand and learn from one another. Two of the most fundamental features of collaborative learning are the grouping of students and the gaining of knowledge through social interactions with peers. The term “blended learning” refers to a new approach to education that combines conventional and contemporary learning models, in which students' interaction with and education from their digital devices does not totally replace their interaction and education from their traditional teachers. However, there are a number of obstacles that prevent educators from fully grasping blended learning models and putting them into practice. There have been a number of difficulties in implementing blended learning models due to the varying degrees to which they are accepted and used. In this article, we talked about collaborative learning data analysis since it helps teachers determine if their pedagogical approaches are working and where they may be strengthened. These results have the potential to contribute to a more comprehensive knowledge of (Artificial Intelligence in Education) AIED and its consequences for educational policies, educational AI design, and instructional design geared toward improving (The School Advisory Council) SAC in education.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 2","pages":"Article 100478"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50181853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emotional heterogeneity and intergenerational knowledge transfer in family firms","authors":"Ana Lucia Caicedo Leitón,&nbsp;Martin Larraza Kintana","doi":"10.1016/j.hitech.2023.100480","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100480","url":null,"abstract":"<div><p>With this study, we attempt to understand how emotions influence the intergenerational transfer of knowledge in family businesses from the perspective of emotional heterogeneity. We use a qualitative methodology of multiple case studies to comprehensively address theissue. The results indicate that emotions influence knowledge transmission and learning, sotransmission is more effective in Enmeshed and Balanced family businesses than in Disengaged ones.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 2","pages":"Article 100480"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50181955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning models for evaluating the benefits of business intelligence systems","authors":"Mano Ashish Tripathi ,&nbsp;Kilaru Madhavi ,&nbsp;V.S. Prasad Kandi ,&nbsp;Vinay Kumar Nassa ,&nbsp;Banitamani Mallik ,&nbsp;M. Kalyan Chakravarthi","doi":"10.1016/j.hitech.2023.100470","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100470","url":null,"abstract":"<div><p>Due to the uncertainty of the market and the intensity of rivalry, business owners and managers are often compelled to experiment with a wide variety of strategies for enhancing their company's performance. By enhancing the timeliness and quality of inputs to the decision-making process, Business Intelligence (BI) is one such idea and tool that combines operational data with analytical tools to show complex and competitive information to planners and decision-makers. Business intelligence (BI) tools help companies rapidly generate insights that guide managers toward operational efficiencies, lead them to new opportunities, and set them apart from the competition. The literature study shows that there is a debate about whether BI tools have an effect on the quality of decisions and the development of businesses. The present research explores the varied empirical facets of BI application through ML models. This study concluded with a discussion of how Machine Learning models can be used to assess the value of BI tools. Machine learning models, fed with historical data and a wealth of input features, can foresee the effect of new systems on metrics like revenue development, customer behavior, and inventory management. Using these models, businesses will be able to better evaluate potential investments in new tools and systems.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 2","pages":"Article 100470"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50181919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT-based data quality and data preprocessing of multinational corporations","authors":"N. Sirisha ,&nbsp;M. Gopikrishna ,&nbsp;P. Ramadevi ,&nbsp;Raveendranadh Bokka ,&nbsp;K.V.B. Ganesh ,&nbsp;M. Kalyan Chakravarthi","doi":"10.1016/j.hitech.2023.100477","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100477","url":null,"abstract":"<div><p>Increasing numbers of devices that output large amounts of geographically referenced data are being deployed as the Internet of Things (IoT) continues to expand. Partly as a result of the IoT's dynamic, decentralized, and heterogeneous architecture. These are all examples of the Internet of items (IoT), despite the fact that we might be thinking that one of these items is different from the others. The physical and digital worlds are connected by the Internet of Things (IoT). Nowadays, one of the key goals of the Internet is its own development. This paper provides an in-depth analysis of IoT-based data quality and data preparation strategies developed with multinational corporations in mind. The goal is to make IoT data more trustworthy and practical so that MNCs may use it to their advantage in making educated business decisions. The proposed structure consists of three distinct actions: gathering data, evaluating data quality, and cleaning up raw data. Data preprocessing research is essential since it decides and significantly affects the accuracy of predictions made in later stages. Thus, the recommendation for a special and useful combination in the framework of different data preprocessing task types, which includes the following four technical elements and is briefly justified, is made. The Internet of Things (IoT) is a design pattern in which commonplace items can be equipped with classification, sensing, networking, and processing capabilities that will enable them to communicate with one another over the Internet to fulfill a specific function. The Internet of Things will eventually change physical objects into virtual objects with intelligence. In addition to a detailed analysis of the IoT layer, this article gives an overview of the existing Internet of Things (IoT), technical specifics, and applications in this recently growing field. However, this publication will provide future scholars who desire to conduct study in this area of Internet of Things with a better knowledge.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 2","pages":"Article 100477"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50181851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of the CEO's personality traits of SME on the orchestration of big data","authors":"Jean-Sébastien Lacam ,&nbsp;David Salvetat","doi":"10.1016/j.hitech.2023.100451","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100451","url":null,"abstract":"<div><p>This research examines the influence of the CEO's personality traits on big data orchestration in French high-tech SMEs. We suggest that CEOs' openness, conscientiousness, extroversion, agreeableness or neuroticism guides their ability to use data. This empirical study of 106 CEOs reveals that their psychological attributes drive four types of data management and a three-step data orchestration capability. Being emotional stability, open, outgoing and conscientious is good for orchestrating the 6Vs of the big data model. Conversely, CEOs who present negative and closed personality traits have little interest in data. Their data orchestration varies according to their personality.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 1","pages":"Article 100451"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CEO overconfidence and subsequent firm performance an indirect effect via earnings manipulations","authors":"Amel Kouaib","doi":"10.1016/j.hitech.2023.100452","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100452","url":null,"abstract":"<div><p>Empirical evidence on the indirect effect of chief executive officer (CEO) overconfidence on future firm performance are missing. Corroborating upper echelons theory, this study identifies this effect exploiting the earnings manipulation methods as a mediator. Tobin's Q is used as a proxy for firm performance and financial press-based measure is used to capture CEO overconfidence level through articles counts. Data from 4 European samples listed on Stoxx Europe 600 Index: AEM sample (2305 observations and 461 firms), CFO-REM sample (1770 observations and 354 firms), PR-REM sample (1350 observations and 270 firms) and RD-REM sample (1295 observations and 259 firms), for the period 2010 to 2020 are used to test the mediation model of Baron and Kenny (1986). Evidence reveals that AEM/REM practices partially mediate the relationship between CEO overconfidence and subsequent firm performance. Findings from this paper can be of interest for accounting regulators, since that CEO overconfidence level can impact future performance through AEM and REM. Increasing scrutiny over AEM does not eliminate other modalities. It changes the preference of an overconfident CEO for different strategies even if they are costly for investors.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 1","pages":"Article 100452"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Family firms' innovation: The indirect effects of socioemotional wealth and the role of social capital","authors":"Michele Stasa,&nbsp;Ondřej Machek","doi":"10.1016/j.hitech.2023.100454","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100454","url":null,"abstract":"<div><p>The paper aims to examine the role of socioemotional wealth (SEW), social capital (SC), and knowledge internalization in the innovation process of family firms. It draws upon recent shortcomings of management and family business literature including the need for exploring mediators in the innovation process and innovation heterogeneity of family businesses. A serial mediation model is tested on a dataset of 198 US family firms. We found SEW to be positively related to bonding SC. In turn, bonding SC was found to enhance bridging SC and knowledge internalization. Despite a negative relationship between bonding SC and innovation output, our analysis revealed three indirect paths that demonstrate the crucial role of SEW and bonding SC in promoting innovation in family firms. Specifically, SEW increases innovation output through the sequential effect of (1) bonding SC and bridging SC, (2) bonding SC and knowledge internalization, and (3) bonding SC, bridging SC, and knowledge internalization. The implications for theory and practice are discussed.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 1","pages":"Article 100454"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spurring sustainability commitment strategy of family-owned SMEs: A multi-analytical SEM & ANFIS perspective","authors":"Khai Wah Khaw ,&nbsp;Abdullah Mohammed Sadaa ,&nbsp;Alhamzah Alnoor ,&nbsp;Ali Shakir Zaidan ,&nbsp;Yuvaraj Ganesan ,&nbsp;XinYing Chew","doi":"10.1016/j.hitech.2023.100453","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100453","url":null,"abstract":"<div><p>The study seeks to understand the impact of leadership styles on the organizational strategy of sustainability commitment, utilizing the mediator role of employees' eco-friendly practices in family firms in Malaysia. To achieve this aim, dual-stage structural equation modeling (SEM) and neuro-fuzzy inference system (ANFIS) were employed to analyze the leadership style of Malaysian family firms. This study collects empirical data from 427 employees belonging to Malaysian family firms via a structured questionnaire. Results demonstrate supportive, authentic, and paternalistic leadership styles affect the sustainability commitment strategy of organizations. Moreover, employees' eco-friendly best practices fully mediate the link between leadership styles and the organizational strategy of sustainability commitment. Findings of non-compensatory relationships also indicated employees' eco-friendly best practices is the most determinant of the strategy of sustainability commitment, followed by authentic leadership, supportive leadership, and paternalistic leadership.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 1","pages":"Article 100453"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of digital transformation in improving the efficacy of healthcare: A systematic review","authors":"Marcelo Dionisio ,&nbsp;Sylvio Jorge de Souza Junior ,&nbsp;Fábio Paula ,&nbsp;Paulo César Pellanda","doi":"10.1016/j.hitech.2022.100442","DOIUrl":"https://doi.org/10.1016/j.hitech.2022.100442","url":null,"abstract":"<div><p>Healthcare is of high importance, and today it has been undergoing a profound change and being reshaped due to the adoption of digital transformations (DT). Nevertheless, considering the quick evolution of these technologies and its massive adoption, the current literature is still incipient on how innovations are translated into practice, with limited empirical evidence of its effectiveness, problems, and applications in digital healthcare. In fact, despite a considerably growing body of research literature on the use of new technologies in healthcare there is no systematic analysis on the practice of DT in healthcare systems. We believe that a literature review to fill this gap is important and pertinent, especially through the lens of applications, benefits, opportunities, and threats to analyze the status of these implementations and its impact in healthcare systems. We chose to perform a systematic literature review because it is a methodology that reviews previous literature and bring the field together, with rigor, concision, and minimal room for subjectivity, offering transparency in data collection and results with a higher level of objectivity and reproducibility. We expect to advance the understanding of the development of technical innovations in healthcare ecosystem and support scholars and practitioners to further explore empirical evidence of the effectiveness of digital applications in healthcare. We also offer a research agenda based on a bibliographic coupling analysis that identifies theoretical trends in the field researched and indicated four clusters with the most relevant themes of literature: IoT and Cloud computing, Data security, Healthcare structure, and Blockchain.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 1","pages":"Article 100442"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning models for forecasting and estimation of business operations","authors":"Shaik Fayaz Ahamed ,&nbsp;A. Vijayasankar ,&nbsp;M. Thenmozhi ,&nbsp;S. Rajendar ,&nbsp;P. Bindu ,&nbsp;T. Subha Mastan Rao","doi":"10.1016/j.hitech.2023.100455","DOIUrl":"https://doi.org/10.1016/j.hitech.2023.100455","url":null,"abstract":"<div><p>Machine Learning (ML) systems are built to shift through large amounts of data. Applying ML in production settings allows for the collection of additional data that can be used to guide future decisions about the system's design. Since the late 1970s, academics have taken an interest in the field of financial predictions. The real business environment has neglected statistical methods in forecasting, despite highly sophisticated models and rising competence in econometrics and economics studies. Current research centres on implementing various algorithms to identify the variation in performance for each product, and it compares the time series models to one another to identify the better model. A basic forecast model can make reliable, fact-based sales projections, as suggested by the books on forecasting. The worth of the forecast model lies in its ability to simplify the arduous tasks of budgeting and rolling forecasting by providing an unbiased forecast upon which a comprehensive financial strategy can be based. In this research, we first look for appropriate machine learning algorithms that can be used to predict sales of truck components, and then we run experiments with the selected algorithms to make predictions about sales and assess how well they work. Business forecasting allows for the estimation of a wide variety of activities, each of which can be tailored to the individual requirements of the company. Here are a few examples of frequently estimated kinds of operations. Although it is well-known that certain algorithms, such as Simple Linear Regression, Gradient Boosting Regression, Support Vector Regression, and Random Forest Regression, outperform others, it has been demonstrated that Random Forest Regression is the most suitable algorithm. Based on the results of the experiments and the analysis, the Ridge regression algorithm was selected as the best algorithm to conduct the sales forecasting of truck components for the selected data.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 1","pages":"Article 100455"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50200560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}