Search results

This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

Recent research has highlighted limited explicit knowledge of a company’s strategy on the part of its employees. The concept of Open Strategy has emerged as a promising approach to address and enhance knowledge and understanding of an organization’s strategy across all hierarchical levels. However, empirical investigations of the relationship between the degree of openness in a strategy process and employees’ strategy knowledge remain scarce. This study aims to fill this gap.

We conducted an online survey of individuals from various organizations, measuring the degree of openness of their strategy process and using regression analysis to determine the impact on strategy knowledge among the respondents. We operationalized strategy knowledge using two concepts. The first concept encompassed general knowledge of the employer’s strategy, mission, and vision. The second concept measured strategy knowledge in a more concrete manner by assessing factors such as the company’s markets, how it positions itself and its economic logic.

Our findings indicate that regardless of the level of hierarchy, the degree of openness has a strong positive effect on strategy knowledge. However, this effect is smaller when specific aspects of strategy knowledge are assessed compared to a general understanding of strategy. Our results highlight the potential of Open Strategy as an effective approach to enhancing employees’ knowledge of the employer’s strategy.

Our research demonstrates that the degree of openness in strategy processes significantly enhances strategy knowledge, thereby laying the groundwork for effective strategy implementation. The results highlight the importance of cultivating a culture of transparency, participation and inclusion of employees as well as ongoing communication to effectively integrate strategy into organizational practices. We offer practical implications for managers striving to optimize their strategic management processes.

Minimizing the impact on the surrounding environment and maximizing the use of production raw materials while ensuring that the relevant processes and services can be delivered within the specified time are the contents of enterprise supply chain management in the green financial system.

With the continuous development of China's economy and the continuous deepening of the concept of sustainable development, how to further upgrade the enterprise supply chain management is an urgent need to solve. How to maximize the utilization of resources in the supply chain needs to be realized from the whole process of raw material purchase, transportation and processing.

It was proved that digital twin technology had a partial intermediary role in the role of supply chain big data analysis capability on corporate finance, market, operation and other performance.

This paper focused on describing how digital twin technology could be applied to big data analysis of enterprise supply chain under the green financial system and proved its usability through experiments. The experimental results showed that the indirect effect of the path big data analysis capability digital twin technology enterprise financial performance was 0.378. The indirect effect of the path big data analysis capability digital twin technology enterprise market performance was 0.341. The indirect effect of the path big data analysis capability digital twin technology enterprise operational performance was 0.374.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

The authors aimed to examine how the level of digitalization in Poland and Ukraine affects the contribution of small and medium-sized enterprises (SMEs) to the countries’ gross domestic product (GDP).

The study involved a comparative analysis and statistical modeling of the impact of key economic factors on the contribution of SMEs to Poland’s and Ukraine’s GDP in the 2010–2020 period. The authors used principles of the theory of economic growth and calculated the coefficient of digital competitiveness as a composite indicator consisting of a number of global indices.

The study revealed significant differences between both countries, which can be attributed to a higher level of digitalization in Polish SMEs. The authors used the Polish experience to recommend how to reform Ukraine’s digital economy in postwar recovery.

The contribution of SMEs to Poland’s GDP is higher than that of Ukraine’s because of the higher entrepreneurship rate in the Polish micro and small enterprises (MSEs) sector. The authors found that a unit change in the integrated coefficient of digital competitiveness is related to the greatest change in the contribution of SMEs to the country’s GDP when the other factors in the model equation remain fixed.

This paper seeks to explore the effect of performance duration (rather than intensity) on the subsequent initiation of strategic change by firms. Specifically, the effect of outperformance and underperformance duration on strategic change, as well as the moderating effect of environmental dynamism, is studied.

Using a fixed-effects model, analyzing a sample of 34,907 firm-year observations from 1980 to 2018 across 112 industries mostly supported proposed hypotheses.

Results revealed a U-shaped relationship between outperformance duration and strategic change and an inverted U-shaped relationship between underperformance duration and strategic change. The moderation role of environmental dynamism was only partially supported.

This study examines a new dimension of performance feedback, namely duration, rather than the widely used intensity of performance feedback, to enhance our understanding of the behavioral theory of the firm.

This study examines the relationship between internal control and corporate environmental responsibility.

Unlike US studies that concentrate solely on internal control over financial reporting, this study uses a comprehensive index that encompasses internal control over financial reporting, operations, and compliance. Corporate environmental responsibility is measured by environmental investments. Our research sample comprises Chinese listed firms from 2010 to 2018.

The results demonstrate a positive correlation between internal control and corporate environmental investments. Furthermore, we find that firms with high-quality internal control can improve their financial and environmental performance through environmental investments. After decomposing internal control into its five components, we show that the control environment, control activities, and information and communication components exhibit stronger effects on environmental investments than the risk assessment and monitoring components. Finally, the cross-sectional analyses reveal that the positive effect of internal control is more pronounced in private firms and in firms that are subject to weaker environmental regulation.

By focusing on the effect of a comprehensive internal mechanism on corporate environmental responsibility in China, this study contributes to the literature in developed-country settings that overwhelmingly focuses on the impact of external stakeholders and regulations.

Utilizing datasets of Ecuador, Hungary, Kazakhstan, Mexico and Peru from the Programme for the International Assessment of Adult Competencies survey from 2017 to 2018, this study aimed to develop and validate a profile indicating core workplace skills in developing countries.

DeVellis' guide of scale development navigated the development of the profile. Multiple techniques including item analysis, exploratory factor analysis, confirmatory factor analysis and multigroup confirmatory factor analysis were used on a sample of 7,166 participants to validate the profile of core workplace skills in developing countries.

A resultant five-dimensional profile with 18 items was developed: oral communication skills, reading skills, math skills, information and communication technology skills and learning skills. The estimates of composite reliability showed the profile was reliable. The validity estimates of the profile were obtained from several sources including content, convergent, discriminative and construct validity. The measurement invariance was also held for the profile.

Based on the researcher's knowledge, the study is the first attempt to develop a profile to indicate core workplace skills in developing countries. The profile theoretically framed the core workplace skills in developing countries and provides a new measure for identifying, evaluating and thus improving core workplace skills in developing countries for different stakeholders in the era of Education 4.0.

The purpose of this paper is to explore existing and desired methodologies for systematic quality work to promote quality in preschools from the principal’s perspective.

A collaborative approach was used in this research project, and principals were asked to complete portfolio assignments. Their answers to those portfolio assignments were analysed by the research team and subsequently compared to total quality management values.

Existing and desired methodologies for systematic quality work are presented and sorted into 13 and 17 groups, respectively. The principals desire four times more methodologies than they are presently using to promote systematic quality work, and the results show that they must extend their methodologies to support TQM values.

This research is based on answers collected from 18 principals in one municipality in Sweden.

The use of the cornerstone model provides a framework to illustrate the application of TQM in preschools.

Principals struggle to find time for systemic quality work. The presented results can be used to work systematically with quality in preschools and other organizations.