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Previous experience is a critical factor affecting entrepreneurial activities; however, it has not been fully studied in the existing literature. This study attempts to comprehensively reveal the routes and mechanisms of occupational experience that affect entrepreneurial activities and assess the entrepreneurial potential of different occupational practitioners.

By matching occupational characteristics with entrepreneurs' competence, this study proposes ten hypotheses about how occupational experience affects entrepreneurial entry and performance. This empirical study is based on the Occupational Information Network database and Chinese survey data. Factor and regression analyses were used in the empirical research.

This study verifies that different occupational practitioners have varied entrepreneurial potential. Occupational experience, including occupational uncertainty, market contact and social capital, gained from previous experience significantly affects entrepreneurial entry. Meanwhile, occupational characteristics, including management experience, marketing experience, social capital, financial capital, risk-taking ability and creativity, accumulated from previous experience, have a significant impact on entrepreneurial performance.

This study is a pioneering attempt to reveal the relationship between occupational experience and entrepreneurial activities. The transmission mechanism of previous experiences affecting entrepreneurial activities is comprehensively revealed by relaxing the assumption of a representative occupation. These findings provide a theoretical foundation for empirical evidence and have important practical value.

This research paper is concerned with the problem of making “knowledge flow” practical for a wide variety of companies. The paper discusses the need for a practical solution to knowledge transfer and demonstrates how the challenges of knowledge transfer were overcome in a cross‐functional environment using the principles of object technology. The proposed concept, by offering a structured approach to knowledge transfer, allows organisations to create systems based on simple but effective knowledge transfer processes, which will integrate with legacy computing systems.

Despite the increasing interest in the role of supply chain concentration (SCC) in improving performance, its influence on firms' sustainability performance remains unexplored, as do the underlying mechanisms of this relationship. Drawing on resource dependence theory, the authors investigate the relationship between SCC and manufacturing firms' sustainability performance and the moderating roles of operational slack and information transparency.

The authors use secondary data from 3,581 manufacturing firms listed on the Shanghai and Shenzhen A-share stock markets from 2006 to 2020 to conduct an empirical analysis using panel data regression models.

Manufacturing firms' SCC is negatively related to sustainability performance until it reaches a certain point, where SCC positively affects sustainability performance, presenting a U-shaped relationship. In addition, operational slack represented by a quick ratio moderates the relationship between SCC and sustainability performance by flattening the curve. Operational slack represented by receivable turnover ratio moderates the relationship between SCC and sustainability performance by steepening the curve and shifting the turning point left. Information transparency strengthens the effect of SCC on the sustainability performance by steepening the curve.

This investigation provides a comprehensive view of the SCC– sustainability performance relationship.

To explore the mechanism that shapes firms' supply chain learning (SCL) practices, this study examines the relationship between firms' knowledge network embeddedness and their SCL practice in a supply chain network, as well as the moderating role of supply chain network cohesion in this relationship.

Using patent application data and supply chain partner information from 869 listed firms between 2011 and 2020 in China, this study uses fixed-effect regression models to reduce endogeneity problems by controlling for individual heterogeneity effects that cannot be observed over time.

Firms' knowledge network embeddedness has an inverted U-shaped effect on their SCL, and this non-linear relationship is conditional on supply chain network cohesion, which strengthens (weakens) the positive (negative) effect of knowledge network embeddedness on SCL.

The findings show that managers can reconcile the downsides of knowledge network embeddedness on SCL by fostering greater supply chain network cohesion.

Drawing from the network pluralism perspective, this study contributes to supply chain literature by extending the research context of the antecedents of SCL from a single-network setting to a dual-network setting. It extends the network pluralism perspective by showing that not only positive effects but also negative effects of network embeddedness can transfer from one network to another.

Experimental design (ED) is a powerful technique which involves the process of planning and designing an experiment so that appropriate data can be collected and then analysed by statistical methods, resulting in objective and valid conclusions. It is an alternative to the traditional inefficient and unreliable one‐factor‐at‐a‐time approach to experimentation, where an experimenter generally varies one factor or process parameter at a time keeping all other factors at a constant level. This paper presents a step‐by‐step approach to the optimisation of a production process (of retaining a metal ring in a plastic body by a hot forming method) through the utilisation of Taguchi methods of experimental design. The experiment enabled the behaviour of the system to be understood by the engineering team in a short period of time and resulted in significantly improved performance (with the opportunity to design further experiments for possible greater improvements).

Today’s business world is facing a plethora of managerial and technological changes which are beyond the capacity of any firm to control or absorb. Customer satisfaction, development of new products, and introduction of new technologies are well‐known driving forces, but their fast mutation and turmoil are making them unpredictable. Companies have to radically alter their strategic and process goals to keep up with this volatile market. In this turbulent environment, business process reengineering (BPR) has evolved as the most promising approach for designing organizations. It is extremely important for reengineers to understand the “driving” forces in this environment. One of the most important and fundamental drivers is understanding the goals (goal mapping) of the organization. Typically the goals of organizations are derived from the “voice of the customer”. In this paper we address a procedure by which optimum level of goal mapping can be considered in the preliminary stages of BPR. A cost optimization model for goal mapping is proposed using an example from the Space Shuttle Testing Facility at the SSC‐National Aeronautics and Space Administration.

Technological process innovation (TPI) is a distinctive organizational phenomenon characterized by a firm-internal locus and underlying components such as mutual adaptation of new technology and existing organization, technological change, organizational change, and systemic impact. The purpose of this paper is to investigate the management of these components at different stages of the innovation lifecycle (ILC) in large manufacturing companies.

The authors adopt an exploratory case-based research design and conduct a multiple case study of five large successful manufacturing companies operating in different industries in Germany. The authors build the study on 55 semi-structured interviews, which yielded 91.5 hours of recorded interview data. The authors apply cross-case synthesis and replication logic to identify patterns of how companies address process innovation components at different ILC stages.

The study uncovers the content of four central TPI components across the ILC and identifies differences between the development of core and non-core processes. Based on the findings the authors describe asymmetric adaptation as a theoretical construct and propose that companies seek different levels of process standardization depending on the type of process they develop, which in turn affects whether there is a greater extent of technological or organizational change.

Awareness of existing structures, processes, and technologies, as well as their value in relation to the company’s core and non-core operations is imperative to determining the adequate structure of mutual adaptation.

The authors provide detailed insight on the management of mutual adaptation, technological, and organizational change, as well as systemic impact at the different stages of the ILC. The authors extend prior research by adopting an ILC perspective for the investigation of these four TPI components and by proposing a construct of asymmetric adaptation to capture key mechanisms of process development and implementation.

This paper aims to develop a diffusion model that can be used to understand and forecast the market growth of service products in a competitive environment. Despite the fast growth of the service sector, the existing literature has dedicated little effort to modeling the market growth of service products.

The authors propose a choice-type diffusion model that links the issues of service product utility, customers’ choice preference, customer switching behavior and the market growth of service products. The authors use the market data of one online product and assess the performance of the proposed model using this case.

The results demonstrate the model’s good fitting and forecasting performance. Specifically, the proposed model has better performance than the benchmarks the authors choose from the existing literature.

This study shows that market growth of service products can have different diffusion patterns with that of durable goods, which is evidence of the needs for specific models for service diffusion. Further, this study demonstrates the important role of customer switching in service diffusion. Also for marketing practitioners, this study provides an explanation and forecasting tool for the market growth of service products, which can be used for marketing planning in the service industry.

The purpose of this paper is to compare perspectives on humanitarian logistics (HL) and supply chain management (SCM) among programmes and logistics/support staff.

Underpinned by services supply chain management (SSCM) theory, a single case study of a leading international non-governmental organisation is presented based on a web-based survey of the organisation’s global operations staff, supplemented by semi-structured interviews conducted with senior representatives.

The study is believed to be the first to consider the different perspectives of programmes and logistics staff on the interpretation of logistics and SCM. The results indicate both significant divergence between the views of these two cohorts, as well as a general lack of clarity over the concept of SCM, its relationship with logistics and the cross-functional nature of SCM.

Insufficient responses from programme staff limit the generalisability of the findings. Suggestions for future research include further examination of the potential of applying SSCM and demand chain management concepts to the humanitarian context.

The results support the notion that a broader, more strategic interpretation of SCM, more clearly distinguished from the practice of HL, may assist in breaking down perceived jurisdictional boundaries, bridging the gap between programmes and logistics teams, and strengthening demand-chain influences and the “voice of the beneficiary”.

By taking into account the views of non-logisticians, a broader, cross-functional interpretation of SCM is offered leading to revised definitions for both SCM and HL within this sector, together with a framework that integrates SCM across humanitarian relief and development contexts.

The authors developed and validated an algorithm using health administrative data to identify patients who are attached or uncertainly attached to a primary care provider (PCP) using patient responses to a survey conducted in Ontario, Canada.

The authors conducted a validation study using as a reference standard respondents to a community-based survey who indicated they did or did not have a PCP. The authors developed and tested health administrative algorithms against this reference standard. The authors calculated the sensitivity, specificity positive predictive value (PPV) and negative predictive value (NPV) on the final patient attachment algorithm. The authors then applied the attachment algorithm to the 2017 Ontario population.

The patient attachment algorithm had an excellent sensitivity (90.5%) and PPV (96.8%), though modest specificity (46.1%) and a low NPV (21.3%). This means that the algorithm assigned survey respondents as being attached to a PCP and when in fact they said they had a PCP, yet a significant proportion of those found to be uncertainly attached had indicated they did have a PCP. In 2017, most people in Ontario, Canada (85.4%) were attached to a PCP but 14.6% were uncertainly attached.

Administrative data for nurse practitioner's encounters and other interprofessional care providers are not currently available. The authors also cannot separately identify primary care visits conducted in walk in clinics using our health administrative data. Finally, the definition of hospital-based healthcare use did not include outpatient specialty care.

Uncertain attachment to a primary health care provider is a recurrent problem that results in inequitable access in health services delivery. Providing annual reports on uncertainly attached patients can help evaluate primary care system changes developed to improve access. This algorithm can be used by health care planners and policy makers to examine the geographic variability and time trends of the uncertainly attached population to inform the development of programs to improve primary care access.

As primary care is an essential component of a person's medical home, identifying regions or high need populations that have higher levels of uncertainly attached patients will help target programs to support their primary care access and needs. Furthermore, this approach will be useful in future research to determine the health impacts of uncertain attachment to primary care, especially in view of a growing body of the literature highlighting the importance of primary care continuity.

This patient attachment algorithm is the first to use existing health administrative data validated with responses from a patient survey. Using patient surveys alone to assess attachment levels is expensive and time consuming to complete. They can also be subject to poor response rates and recall bias. Utilizing existing health administrative data provides more accurate, timely estimates of patient attachment for everyone in the population.