Journal of High Technology Management Research最新文献

As time went on, technological progress inevitably altered our daily routines. Many new technologies, such as the Internet of Things (IoT) and cryptocurrency, offer revolutionary possibilities. To put it simply, the blockchain is a distributed, public, and auditable database that can be used to record financial transactions. The IoT, or “Internet of Things,” is a system of interconnected electronic devices that can communicate with one another and be remotely monitored and handled. This paper reviews the most recent findings in the field of blockchain and Internet of Things with the goal of examining blockchain as a possible answer to secure IoT data management within supply networks. There is a dearth of literature in the early stages of both blockchain and IoT study because they are such novel topics. The study's findings suggest that in order to improve their leadership quality to intentionally impact employee performance, industry managers should pay attention to human resource management indicators like collaboration, involvement, actualization, perception, and teamwork. This is primarily because of the inherent limitations of IoT devices and the distributed ledger architecture of the blockchain technology. There is potential for IoT to provide many advantages if blockchain capabilities can be optimized for it.

Undoubtedly, the drone industry is one of the fastest-growing industries in the world today. There is unlimited potential for Drone technology with continued growth and investment which are essential to categorize drones as an emerging technology. So, the drone industry is the strongest case for an emerging business industry. The number of industries benefiting from drone technology continues to grow. The emerging drone technology and the advancement of the Indian drone business industry are cause and effect relation which are growing and making positive impact across the global drone business industry. We provide an overview and interrelationship of the emerging drone technology and advancement of Indian drone business industry as there is no review to date, has offered a wholistic retrospection of this kind of research review and address this gap. So, this manuscript aims to provide readers with a high-level overview and review of business developments in widely available unmanned aerial vehicles (UAVs), as well as a short summary of the global drone industry and studies that have been covered on drone business industry growth in India over the past decade. This review paper provides a guide that can be used to make sense of the emerging drone business industry and its effect on ever growing drone business in India. The purpose of this review report is to provide a comprehensive market study for the drone business industry that covers a variety of topics, such as relevant facts, relevant historical data, industry-validated market statistics, and predictions based on a systematic literature review (SLR) methodology and set of assumptions that are acceptable. This literature review is longitudinal, and qualitative in nature.

Automatic software fault detection and repair is made possible by autonomic software recovery. By incorporating this function, the software will run more efficiently and aggressively while requiring much less time and resources for maintenance. The focus of this article is on a suggested automated approach to Software Fault Detection and Recovery. The Software Fault Detection and Recovery (SFDR) procedure identifies when a software component has been damaged due to a fault and then restores the damaged component so that the software can continue functioning normally. During the design process, the SFDR is examined and created independently from the intended program. The proposed technique was implemented into an application that demonstrates the SFDR's performance and effectiveness to guarantee its practicality in real-world scenarios. The results of this experiment were encouraging. Results from experiments and comparisons to prior works show that the proposed methodology is successful.

The Internet of Things (IoT) has sparked a revolution in the manufacturing sector, providing numerous advantages to companies that adopt it. Using IoT, factories can boost productivity, cut expenses, and develop a more sustainable business model. The rise of digital networking and real-time communication are compelling manufacturers to adopt cutting-edge technologies in order to compete in today's fast-paced, international marketplace. The Internet of Things (IoT) to facilitate the virtualization of manufacturing processes and the gathering of real-time data to guarantee seamless supply chain operations. There has been abductive qualitative research done. Case studies of the heavy-duty vehicle sector provided empirical data, while a review of the relevant literature provided the theoretical underpinnings. Information system issues and people and structure issues were cited as barriers to analytics adoption. In this study works on challenges and security of manufacturing. Finally, suitable themes for analysis have been derived using a thematic analysis. The results show that manufacturing firms can benefit from analytics solutions for production activities even if they are not highly automated or complicated. The Internet of Things (IoT) offers numerous opportunities for growth in the business models of manufacturing companies. Businesses can boost efficiency, cut expenses, and develop a more robust business model by implementing IoT. Successfully integrating IoT, however, calls for meticulous preparation and execution.

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.

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.

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.

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.

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.

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.