Search results

This study aims to understand how practitioners use their insights in software development models to share experiences within and between organizations.

This is a qualitative study of practitioners in software development projects, in large-, medium- or small-size businesses. It analyzes interview material in three-step iterations to understand reflexive practice when using software development models.

The study shows how work processes are based on team members’ experiences and common views. This study highlights the challenges of organizational learning in system development projects. Current practice is unreflective, habitual and lacks systematic ways to address recurring problems and share information within and between organizations. Learning is episodic and sporadic. Knowledge from previous experience is individual not organizational.

Software development teams and organizations tend to learn about, and adopt, software development models episodically. This research expands understanding of how organizational learning takes place within and between organizations with practitioners who participate in teams. Learnings show the potential for further research to determine how new curriculums might be formed for teaching software development model improvements.

Effective safety supervision plays a crucial role in ensuring safe production within coal mines. Conventional coal mine safety supervision (CMSS) in China has suffered from the problems of power-seeking, excessive resource consumption and poor timeliness. This paper aims to explore the Internet+ CMSS mode being emerged in China.

The evolution of CMSS systems underwent comprehensive scrutiny through a blend of qualitative and quantitative approaches. First, evolutionary game theory was used to analyze the necessity of incorporating Internet+ technology. Second, a system dynamics model of Internet+ CMSS was crafted, encompassing a system flow diagram and equations for various variables. The model was subsequently simulated by taking the W coal mine in Shanxi Province as a representative case study.

It was revealed that the expected safety profit from the Internet+ mode is 296.03% more than that from the conventional mode. The precise dissemination of law enforcement information was identified as a pivotal approach through which the Internet+ platform served as a conduit to foster synergistic collaboration among diverse elements within the system.

The outcomes of this study not only raise awareness about the potential of Internet+ technology in safety supervision but also establish a vital theoretical foundation for enhancing the efficacy of the Internet+ CMSS mode. The significance of these findings extends to fostering the wholesome and sustainable progress of the coal mining industry.

This research stands out as one of the limited studies that delve into the influence of Internet+ technology on CMSS. Building upon the pivotal approach identified, to the best of authors’ knowledge, a novel “multi-blind” working mechanism for Internet+ CMSS is introduced for the first time.

This paper explores the orchestration of digital innovation in Industry 4.0 organisations.

The study applies the activity theory to explorative multiple case studies. Observations of innovation activities in five business cases take place at two large international organisations.

The results underline five logics of action that drive digital innovation: (1) digital transformation, (2) technology translation, (3) catalyst agents, (4) digital thread and (5) empowerment. Further, the case study organisations highlight the importance of developing a sustainable culture capable of continuously adopting new technologies, processes and infrastructure that will allow the management of digital innovations.

The study empirically shows the motivations and challenges in orchestrating digital innovation in Industry 4.0 organisations.

Policymakers are developing national strategic plans to encourage organizations to adopt Construction 4.0 technologies. However, organizations often adopt the recommended technologies without aligning with organizational vision. Furthermore, there is no prioritization on which Construction 4.0 technology should be adopted, including the impact of the technologies on different criteria such as safety and health. Therefore, this study aims to evaluate Construction 4.0 technologies listed in a national strategic plan that targets the enhancement of safety and health.

A list of Construction 4.0 technologies from a national strategic plan is evaluated using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Then, the data are analyzed using reliability, fuzzy TOPSIS, normalization, Pareto, sensitivity, ranking and correlation analyses.

The analyses identified six Construction 4.0 technologies that are critical in enhancing safety and health: Internet of Things, autonomous construction, big data and predictive analytics, artificial Intelligence, building information modeling and augmented reality and virtualization. In addition, six pairs of Construction 4.0 technologies illustrate strong relationships.

This study contributes to the existing body of knowledge by ranking a list of Construction 4.0 technologies in a national strategic plan that targets the enhancement of safety and health. Decision-makers can use the study findings to prioritize the technologies during the adoption process. Also, to the best of the authors’ knowledge, this study is the first to evaluate the impact of Construction 4.0 technologies listed in a national strategic plan on a specific criterion.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

Properly allocating an organization's activities within a building is vital to reducing the relational complexity arising from process–environment interactions. Multiple relationships are mapped, and certain interferences are only identified after these have been processed. The method/software employed for this task is Mapping Activity Environment Allocation (MAEA). However, data input and interpretation of results depend on the usability conditions of the organization's agents. This paper presents MAEA's usability test results.

Test sessions and interviews were carried out with seven agents registered at a University Hospital. Participants were instructed to think aloud during its use, and immediately afterward, responded to semi-structured interviews. Test sessions were audio recorded and screen captured.

Participants found the software easy to use and pointed out valuable implications for professional and academic use. In addition to relationship, priority and parallelism data, customized visualizations were created, including organizational charts, flowcharts and activity flow routes on the floor plan.

MAEA's simplicity allows non-designers to conduct evidence-based assessments and decisions. It allows designers to test their proposals during the programming and outline proposal stages.

A more detailed definition of design requirements from the beginning increases the conditions to successfully achieve project goals.

The ability to map the allocation of activity-spaces in the pre-design phase of building architecture allows for early identification of interactions, aiding in the development of more robust project requirements during programming.

The purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.

Based on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.

Digital twin technology enhances the quality and efficiency of assembly process planning and execution system.

It provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

This study focuses on (re-)introducing computer simulation as a part of the research paradigm. Simulation is a widely applied research method in supply chain and operations management. However, leading journals, such as the International Journal of Operations and Production Management, have often been reluctant to accept simulation studies. This study provides guidelines on how to conduct simulation research that advances theory, is relevant, and matters.

This study pooled the viewpoints of the editorial team of the International Journal of Operations and Production Management and authors of simulation studies. The authors debated their views and outlined why simulation is important and what a compelling simulation should look like.

There is an increasing importance of considering uncertainty, an increasing interest in dynamic phenomena, such as the transient response(s) to disruptions, and an increasing need to consider complementary outcomes, such as sustainability, which many researchers believe can be tackled by big data and modern analytical tools. But building, elaborating, and testing theory by purposeful experimentation is the strength of computer simulation. The authors therefore argue that simulation should play an important role in supply chain and operations management research, but for this, it also has to evolve away from simply generating and analyzing data. Four types of simulation research with much promise are outlined: empirical grounded simulation, simulation that establishes causality, simulation that supplements machine learning, artificial intelligence and analytics and simulation for sensitive environments.

This study identifies reasons why simulation is important for understanding and responding to today's business and societal challenges, it provides some guidance on how to design good simulation studies in this context and it links simulation to empirical research and theory going beyond multimethod studies.

This study aims to use organizational fashion to underscore a novel phenomenon in which products, services and practices fade in and out of the tourism/hospitality setting within a specific time frame. Drawing from the fashion theoretical strands in organization research, this paper studies how fashion has been conceptualized, operationalized and then diffused among tourism/hospitality enterprises.

A qualitative case design was used. A total of 37 semistructured in-depth interviews with executives of innovative tourism/hospitality companies (e.g. restaurants, hotels, theme parks and travel agencies) were conducted. This paper focuses on the organizational fashion phenomenon in which organizational trendsetters with creative, “hot” products/services have emerged prominently in the marketplace.

This inquiry illustrates a social phenomenon concerning the organizational fashion setting process by integrating existing production practices among different organizational suppliers in the hospitality sector. Different cases in the study show that fashion consists of a series of hybrid, paradoxical processes. These include conceptualization (conventionalization vs novelty, and personalization vs conformity), operationalization (bundling vs unbundling, and learning vs relearning) and diffusion (framing vs co-framing, and adaptation vs alteration).

Throughout the three continuous processes, service design and identity development for consumption, as well as value creation and knowledge transformation for production, are carried out according to the decision of what is “hot” and what is “out” at a particular time. In essence, fashion helps to explain why hospitality institutions imitate specific innovations to take advantage of popular trends in the consumer market, as well as how such trends vanish eventually.

This research contributes the insight that organizations use fashion as a managerial initiative to translate their organizational goals and improvise nascent products and services. The fashion processes can be triggered by microlevel individual organizations and are spread through a series of social interactions to become macrolevel phenomena in a recurring manner.

This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises.

A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises.

Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation.

The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry’s radical innovation.

This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry’s radical innovation by considering the mediating role of three dimensions of business intelligence.