جستجوی مقالات مرتبط با کلیدواژه « fourth industrial revolution » در نشریات گروه « مدیریت »

The current research was conducted to design and model digital transformation based on the fourth-generation industrial revolution in Iran Khodro Company. In this regard, the main question focuses on identifying the factors that impact the design and modeling of digital transformation, rooted in the fourth-generation industrial revolution, and exploring the pattern of causal relationships among these factors in Iran Khodro. 

This study adopts an applied-developmental approach in terms of its purpose, utilizes a descriptive survey method for data collection, and employs a mixed data analysis method. The community of participants in the qualitative section comprised of experts, policymakers, academics, managers of knowledge-based companies, and senior managers of Iran Khodro Company. Sampling was done in a targeted way and theoretical saturation was achieved with 15 people. The statistical population for the quantitative part comprised experts from Iran Khodro Company, estimated to be 172 individuals using Cochran's formula. The required sample volume was provided by a simple random sampling method. A semi-structured interview and a researcher-made questionnaire were used to collect data. The underlying categories of digital transformation in Iran Khodro Company were identified by the grounded theory method and the final model was validated by the partial least squares method. Qualitative data analysis was conducted using Maxqda software, while the quantitative analysis was performed using Smart PLS.

To explain the digital transformation model based on the fourth-generation industrial revolution in Iran Khodro company, semi-structured expert interviews were conducted with marketing professors and managers of Iran Khodro company. At this stage, before the start of the interview, six open questions were considered and during the interview process, new questions were also raised as expected. To get familiar with the depth and scope of the content of the data, repeated and active reading of the data (searching for meanings and patterns) were done. The results of the interviews were extracted using the grounded theory method based on the theory of Strauss and Corbin and by open, central, and selective coding. In the open coding stage, 256 codes were first identified, duplicate codes were removed and synonyms were combined, and finally 58 open codes were identified. During the central coding stage, the initial 58 codes were categorized into 10 classes, and in the selective coding stage, these 10 classes were further condensed into six main classes. In the process of theorizing the database, after collecting, analyzing, and interpreting the data, the next step is to present the model, draw conclusions, and summarize the research. By examining the current situation, the obtained data were classified into six main categories. Professors and experts contributed their perspectives, leading to the utilization of 58 indicators to elucidate the digital transformation model rooted in the fourth-generation industrial revolution at Iran Khodro Company, drawn from all indicators derived through qualitative analysis of interview content.

Based on the research paradigm model, it was determined that digital infrastructure, digital leadership, digital human resources, and digital marketing affect digital transformation in Iran Khodro Company. The fourth industrial revolution provides the necessary platform and the competitiveness of Iran Khodro plays an intervening role. Finally, the digital strategy of Iran Khodro Company leads to the financial and non-financial performance of the company.

Today, with the arrival of new technologies brought by the fourth industrial revolution, organizations can improve their productivity. And considering the many challenges that exist in the country's motorcycle industry; Including sanctions, and various environmental requirements and pressures, motorcycle after-sales service companies are forced to establish a comprehensive software management system. Also, with the advancement of technology and the introduction of new technologies such as the Internet of Things, services are provided for organizations that can help them have a more efficient organizational architecture than before. The result of this research is the design of an organizational architecture based on the Internet of Things for after-sales service companies in the motorcycle industry. To achieve this goal, the inspection unit of one of the branches of Kavir Motor Company was selected as a case study and its processes were rewritten using a piece of hardware based on the Internet of Things; Then, by choosing the famous Zackman framework as the organizational architecture, the first two lines of this framework were rewritten with the Internet of Things and a new organizational architecture based on the Internet of Things was created. In this research, the Internet of Things is the independent variable and the pillars of Zackman's framework (data, function, network, people, time, motivation) are the dependent variables. Based on this, six hypotheses were considered for the research and according to the research questions and hypotheses, a questionnaire was designed for scientific experts in the field of organizational architecture and Internet of Things, and after gathering the opinions of experts and analyzing the questionnaire with chi square test, a questionnaire was also designed for Transportation industry experts (managers and experts in the field of information technology of after-sales service companies) designed and after completion by them and analysis with the third order Wilcoxon test, conclusions were drawn regarding the research hypotheses, which finally out of the six research hypotheses, The fourth hypothesis of the research was rejected, and the first, second, third, fifth and sixth hypotheses were confirmed.

This study examines the factors affecting the model that is presented for the competitiveness of selected knowledge-based companies in the fourth industrial revolution. 23 components were identified and categorized into six factors: causal, contextual, intervening, central phenomenon, strategies and measures, and consequences. Using these components, a questionnaire was designed to determine the current status of selected knowledge-based companies in Iran, and a total of 127 completed questionnaires were collected from 197 knowledge-based manufacturing companies of the first type in the field of electricity and electronics, laser and photonics in the country. was done to examine the current state of the model components. The data was analyzed with SPSS and PLS software. In the analysis carried out, the coefficients of factor loadings of the components are more than 0.4, which shows the appropriateness of this model. Cronbach's alpha and composite reliability is more than 0.7, which indicates the appropriate reliability of the model. Construct validity, convergent validity and divergent validity also had favorable results. The results showed that all the components in the presented model for the competitiveness of selected knowledge-based companies in the fourth industrial revolution played a role, and the current state of all factors except the causal factor, which is in an average state, are in a favorable state.

The Fourth Industrial Revolution affected all industries and transformed the digital, cyber, and real worlds in the supply chains of corporations. Internet of Things (IoT) is one of the emerging technologies that mostly manifests the fourth industrial revolution. As the future of the food industry is tied to the design and management of supply chains enabled by technologies such as the IoT, this paper is to provide a model for developing an IoT-based intelligent transportation technology roadmap based on alternative IoT scenarios in the food-producing cold supply chain.

In this study, scenarios were developed using qualitative methods such as the PESTEL framework and content analysis, based on the critical uncertainty method or GBN, through open semi-structured interviews with experts in the food industry and IoT. After identifying the best IoT technology stock for each selected scenario, a roadmap was developed using the T-plan quick-start method during a tow-day-interactive workshop.

Communication infrastructure and time horizon of technology development were recognized as the most important uncertainties for applying IoT technology in refrigerated transportation of food cold supply chain in producing companies. Finally, “Mutation Alone” and “Ascent Slow” scenarios were selected and technology roadmaps were developed for each scenario in three layers.

Food companies with cold supply chains can use one of the roadmaps presented in this paper as guidelines to equip their transport fleet with IoT technology based on their current situation and choose one of the two selected scenarios. This would enable them to plan, control, and manage the cold transportation chain process by digitally monitoring, tracing, and information sharing in an efficient and effective way.

One of the most important consequences of the fourth industrial revolution is the digitalization of the business environment and the expansion of data-driven marketing. The consequences of these developments will have a profound effect on marketing research. The purpose of this study is to identify the components of value among the large volume of customer feedback in social networks using “Machine Learning” to analyze “Big Data”. This research is descriptive-survey in terms of applied purpose, qualitative in terms of data collection method and qualitative-quantitative (mixed methods) in terms of implementation. For this purpose, the tourism industry is selected as a case study and 8290 customers' opinions are collected from the Internet in this regard and the components of value are extracted using two different methods of "data clustering" and extraction of “association rules". The results of the research in the data clustering section include the identification of 20 value components in relation to the values desired by tourists. Also, using the second method (association rules), seven laws were extracted from the hidden knowledge, in the relationships between the phrases used in the opinions of tourists. In general, the results show that using Big Data analytics and “Machine Learning”, the process of “marketing research” is possible with higher speed and accuracy and lower relative cost.