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Mixed methods research is useful to enhance theoretical and practical research contributions. However, single methods have predominated much logistics and supply chain management (LSCM) research. This paper presents a review of mixed methods research across ten years in LSCM to determine their usage, identify benefits and inhibitors, and provide suggestions for LSCM researchers to realise the benefits from using mixed methods.

This paper adopts a mixed methods approach through a quantitative analysis of methods used in six leading LSCM journals, an e-mail survey of mixed methods article authors during the review period, and four published case studies that used mixed methods.

Only 144 (ten percent) of all empirical articles were published using mixed methods during the review period. A range of benefits and inhibitors regarding mixed methods adoption were found. Suggestions for LSCM authors include research training in mixed methods use and developing a project-specific research design due to the specificity and complexity associated with mixed methods research.

LSCM is at a critical juncture, shaped by new contexts, themes and challenges, and would benefit from different research approaches and methods. This paper contributes to the LSCM domain through analysing the current state, benefits and inhibitors of mixed methods research in LSCM journals to provide a renewed call to action and guidelines for mixed methods LSCM research, and suggesting research design adaptation to enable agile and resilient research when investigating rapidly changing and complex phenomena.

This research intends to develop a model for predicting employees at a high-risk attrition and identify the most important factors affecting them.

In this study, using the triangulation technique of a mixed research method, the employee attrition problem is investigated by identifying its affecting factors. For that matter, data related to the human resources department of a pharmaceutical company in Iran are used. And to achieve the intended goal, advanced data mining algorithms and interviews with human resource managers are applied.

A model for predicting employees at a high-risk attrition is presented based on the gradient boosting machine algorithm with 89% accuracy. The use of the mixed research approach shows that qualitative and quantitative methods can be more effective in identifying the factors affecting employee churn or loss of staff. The results also contain a new situation arising out of the COVID-19 pandemic and remote working scenarios having impact on employee attrition. Finally, human resource policies are presented based on variables related to each of the identified factors.

The novel contributions of this study include real data related to a leading pharmaceutical company as well as a combination of two quantitative and qualitative methods. The hybrid approach can identify the reasons for attrition and, consequently, retention policies to benefit from the advantage of both approaches. Data mining can be useful to identify the factors, which are usually not mentioned in termination interviews, such as direct managers. On the other hand, the results obtained from termination interviews can also include features that the authors cannot identify through data mining, which are specifically related to the characteristics of the pharmaceutical industry such as building a more professional career path. From a practical perspective, since this company specializes in pharmaceutical marketing in a new way and is primarily comprised graduates, it is important to note that the churn of specialized people disperses organizational and technological know-how. On the other hand, the pharmacist community in Iran is small, and their attrition might adversely affect not only the reputation of an organization but the employer's brand as well. So, this research would help other similar firms in retaining their valuable human capital.

This study aims to detect the influential factors and their respective variables that affect the effectiveness or demand-driven level of the industrial parks in Iran. A hybrid procedure is sought to be developed, which embraces both qualitative and quantitative methodologies to identify the aforementioned factors and variables.

This study is incorporated with both qualitative and quantitative methodologies. To implement the qualitative approach, the researchers used focus groups and the related literature. The quantitative methodology has been carried out via a reliable questionnaire that obtained the viewpoints of 700 experts. The reliability of the designed questionnaire has been investigated through Cronbach’s alpha coefficient. By conducting several one-sample t-tests, it was confirmed that the identified factors and variables are significantly influential on the effectiveness of Iran’s industrial parks. The Kruskal–Wallis statistical test was used to determine the priorities of the factors. This research also used a multi-criteria decision-making method, namely, the weighted aggregates sum product assessment (WASPAS) to rank 15 industrial parks of Khorasan province in Iran according to the identified factors.

Comprehensive analyses have been conducted on the identified factors. Results indicate that the infrastructural facilities factor has the highest priority when it comes to affecting the effectiveness of the industrial parks. After that, industrial land and internal factors take the second and the third positions in terms of importance. A total of 15 industrial parks of the aforementioned province have been ranked by the WASPAS. The ranking offered by the WASPAS has been approved by the experts.

Based on the literature investigations, the authors were convinced that there is a scarcity of studies investigating the influential factors that affect the effectiveness or demand-driven level of industrial parks (especially in Iran). Hence, this research has been conducted to propose a procedure equipped with quantitative and qualitative techniques that detect these important factors and their subordinate variables. By means of the developed procedure of this research, it is possible to locate future industrial parks, plan for establishment of future industrial areas and plan for development of current industrial parks.

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.

This paper aims to explore the epistemic connections between three instructional coaches and a first-year in-service teacher during remote planning and debrief meetings. Prior evidence suggests that remote instructional coaching leads to better teaching practices and identifies the instructional coaching moves used to prompt teacher reflection.

In this study, the authors utilized quantitative ethnography and epistemic network analysis (ENA) approaches to explore the epistemic frames of three remote university-based instructional coaches as they supported a first-year in-service teacher.

Quantitative ENA findings shed light on the network connections between instructional coaches and teachers, as well as the epistemic frames observed during planning and debrief meetings. Additionally, the authors provide qualitative findings that complement and reinforce the quantitative results.

All data collection occurred via Zoom, and the class was in a hybrid modality, with some students attending class in person and some attending remotely via Zoom. This unique context could have impacted the epistemic connections surrounding technology and logistics.

This study provides a practical codebook for use in future studies that explores instructional coaching. Findings from this study can be used to inform instructional coaching decisions.

The ENA findings helped deepen the authors' understanding of how instructional coaches can support a first-year in-service teacher during planning and debrief meetings in several ways. Additionally, this study presents a unique context given the COVID-19 pandemic and the remote model of instructional coaching.

This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events.

This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach.

As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges.

Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings.

Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data.

The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges.

The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.

The study proposes an evaluation model that allows quantitative characterization of the effects of service quality on consumer’s satisfaction for online food delivery (OFD) services in a nonlinear manner. As such, the authors endeavor to bridge the research-to-practice gaps whereby the effect magnitudes and nonlinear patterns of service quality have been overlooked in the current literature.

The quantitative Kano method is adopted. A Kano questionnaire was first developed by synthesizing and operationalizing existing evidence on OFD service qualities. The questionnaire solicited consumers’ evaluations of 21 OFD service attributes, and it was distributed to an online panel in Singapore. With 580 valid responses, the functions that quantitatively depict effects of each attribute on consumer’s satisfaction were subsequently derived.

The results reveal that among Singaporean consumers, food quality, reliability of delivery, responsiveness of customer support, ease-of-use of digital interfaces and promotions are pivotal attributes contributing to above-average satisfaction improvement across all performance levels. Meanwhile, delivery riders’ attitudes and real-time tracking functions emerge as substantial contributors to satisfaction at high-performance levels.

The findings provide crucial insights for OFD practitioners in Singapore in resource prioritization and service optimization. This study demonstrated the importance of streamlining customer support services and focusing on the utilitarian aspects of OFD services. Moreover, these results can be employed in advanced service improvement procedures, providing a roadmap for future OFD service enhancements.

This study pioneers the development of a quantitative quality evaluation model in the OFD context. With the established quantitative Kano model, the study addresses the omission of effect magnitudes and nonlinear patterns of service quality. It highlights the transition from a binary “does it affect satisfaction” to a more nuanced “how much does it affect satisfaction” approach, offering a robust understanding of consumer’s satisfaction dynamics.

This study intends to analyze the trend of walkability research over two years (2021–2022) in several aspects, including the research problem, gap and aim underlying walkability issues, research setting, the scale of analysis, the type of data, the data collection tool, the analysis method and contribution.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is employed to conduct a systematic literature review from three leading databases including Web of Science (WoS), Scopus and Sage.

From the review, it can be concluded that the research focuses on two aspects, namely, the built environment and people. The studies in this field are conducted at different scales of the built environment, from macroscale to microscale, including region, city, neighborhood and street-level studies that are conducted around the world including Asia, Europe and America which receive high attention from researchers. Meanwhile, the study of walkability in Australia and Africa gains less attention. It is revealed that there are four factors of walkability including land use and accessibility for macroscale which cover region, city and neighborhood, while street network and pedestrian infrastructure and facility are mainly analyzed for street-level. Most of the studies widely utilize a quantitative approach for analyzing physical walkability factors in a measurement-based approach by using certain tools/software, and a perception-based approach by using questionnaires.

The findings can assist to keep up with the rapid pace of scientific publications and help accelerate the understanding of particular ideas in the field of walkability for the planning and design practice and policy that can be used by researchers/academia, urban designers, urban planners, architects, engineers and policymakers. Also, this study hopefully could assist further research direction for the future and could add value to a more advanced way of understanding the research on walkability.

This research provides a robust framework for walkability trend analysis and a comprehensive understanding of the main ideas from the existing research on walkability worldwide.

This paper aims to contribute to the emerging debate on materiality with novel and original insights about the managerial and theoretical implications related to the adoption of the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB) as reporting standards. Furthermore, the paper will evaluate the main drivers that favor the combination of the two standards by companies to develop new knowledge about the hierarchical relationship between financial and sustainability materiality.

Building on a sample of 2,046 US listed companies observed during the period 2017–2020, the research is conducted using quantitative methods. Multinomial logistic regressions are used to evaluate the differences between GRI and SASB’s adoption.

The analysis highlights that financial and sustainability materiality are driven by different purposes. In detail, SASB’s adoption is driven by factors directly related to financial dynamics, while GRI’s adoption is influenced by the existence of corporate governance mechanisms inspired by sustainable and ethical principles. Furthermore, the last analysis reveals that the combination of the two standards is characterized by the predominance of sustainability materiality.

To the best of the authors’ knowledge, this is the first empirical study on the relationship between financial and sustainability materiality.

Existing conceptual, empirical and case studies evidence suggests that manufacturing industries find the joint implementation of Kaizen philosophy initiatives. However, the existing practices rarely demonstrated in a single framework and implementation procedure in a structure nature. This paper, therefore, aims to develop, validate and practically test a framework and implementation procedure for the implementation of integrated Kaizen in manufacturing industries to attain long-term improvement of operational, innovation, business (financial and marketing) processes, performance and competitiveness.

The study primarily described the problem, extensively reviewed the current state-of-the-art literature and then identified a gap. Based on it, generic and comprehensive integrated framework and implementation procedure is developed. Besides, the study used managers, consultants and academics from various fields to validate a framework and implementation procedure for addressing business concerns. In this case, the primary data was collected through self-administered questionnaire, and 244 valid questionnaires were received and were analyzed. Furthermore, the research verified the practicability of the framework by empirically exploring the current scenario of selected manufacturing companies.

The research discovered innovative framework and six-phase implementation procedure to fill the existing conceptual gap. Furthermore, the survey-based and exploratory empirical analysis of the research demonstrated that the practice of the proposed framework based on structured procedure is valued and companies attain the middling improvements of productivity, delivery time, quality, 5S practice, waste and accident rate by 61.03, 44, 52.53, 95.19, 80.12, and 70.55% respectively. Additionally, the companies saved a total of 14933446 ETH Birr and 5,658 M² free spaces. Even though, the practices and improvements vary from company to company, and even companies unable to practice some of the unique techniques of the identified CI initiatives considered in the proposed framework.

All data collected in the survey came from professionals working for Ethiopian manufacturing companies, universities and government. It is important to highlight that n = 244 is high sample size, which is adequate for a preliminary survey but reinforcing still needs further survey in terms of generalization of the results since there are hundreds of manufacturing companies, consultants and academicians implementing and consulting Kaizen. Therefore, a further study on a wider Ethiopian manufacturing companies, consultants and academic scale would be informative.

This work is very important for Kaizen professionals in the manufacturing industry, academic and government but in particular for senior management and leadership teams. Aside from the main findings on framework development, there is some strong evidence that practice of Kaizen resulted in achieving quantitative (monetary and non-monetary) and qualitative results. Thus, senior management teams should use this research out to practice and analyze the effect of Kaizen on their own organizations. Within the academic community, this study is one of the first focusing on development, validating and practically testing and should aid further study, research and understanding of Kaizen in manufacturing industries.

So far, it is rare to find preceding studies proposed, validated and practically test an integrated Kaizen framework with the context of manufacturing industries. Thus, authors understand that this is the very first research focused on the development of the framework for manufacturing industries continuously to be competitive and could help managers, institutions, practitioners and academicians in Kaizen practice.