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The purpose of this paper is to propose a novel prospect-based two-sided matching decision model for matching supply and demand of technological knowledge assisted by a broker. This model enables the analyst to account for the stakeholders’ psychological behaviours and their impact on the matching decision in an open innovation setting.

The prospect theory and grey relational analysis are used to develop the proposed two-sided matching decision framework.

By properly calibrating model parameters, the case study demonstrates that the proposed approach can be applied to real-world technological knowledge trading in a market for technology (MFT) and yields matching results that are more consistent with the reality.

The proposed model does not differentiate the types of knowledge exchanged (established vs novel, tacit vs codified, general vs specialized) (Ardito et al., 2016, Nielsen and Nielsen, 2009). Moreover, the model focuses on incorporating psychological behaviour of the MFT participants and does not consider their other characteristics.

The proposed model can be applied to achieve a better matching between technological knowledge suppliers and users in a broker-assisted MFT.

A better matching between technological knowledge suppliers and users can enhance the success of open innovation, thereby contributing to the betterment of the society.

This paper furnishes a novel theoretical model for matching supply and demand in a broker-assisted MFT. Methodologically, the proposed model can effectively capture market participants’ psychological considerations.

This paper mainly focuses on how to induce all members to represent members' true preferences for supply and demand matching of E-commerce platform in order to generate stable matching schemes with more social welfare of Multi-agent Matching Platform (MMP) and individually stable advantages than traditional methods.

An MMP is designed. Meanwhile, a true preference inducing method, Lower-Bid Ranking (LBR), is proposed to reduce the number of false preferences, which is helpful to solve the problem that too much false preferences leads to low efficiency of platform operation and supply and demand matching. Then, a systematic model of LBR-based Stable Matching (SM-LBR) is proposed.

To obtain an ideal transaction partner, the adequate preference ordering and modifying according to market environment is needed for everyone, and the platform should give full play to the platforms' information advantages and process historical transaction and cooperation data. Meanwhile, the appropriate supply and demand matching is beneficial to improve the efficiency and quality of platform operation, and the design of matching guidance mechanism is essential.

The numerical experiments show that, the proposed model (SM-LBR) can induce members to represent the model's true preferences for stable matching and generate effective matchings with more social welfare of MMP and individually stable advantages than traditional methods, which may provide necessary method and model reference for the research of stable matching and E-commerce platform operation.

The field of supply chain management (SCM) evolves dramatically due to factors of globalization, innovation, sustainability, and technology. These changes raise challenges not only to higher education institutions, but also to students, employing organizations, and third parties like SCM-related professional bodies. To understand the challenge, the purpose of this paper is to examine the gap between demand and supply of SCM-related knowledge areas, answer-related design questions, and make recommendations to close the gaps.

To compare the demand and supply of SCM-related knowledge areas, demand data is collected from a professional career website and supply data is gathered from operations management (OM) and SCM course syllabi from AACSB-accredited business schools in the USA. Cluster analysis identifies how supply and demand are matched on the data collected.

First, gaps exist between SCM talent requirements from industry and the knowledge/skill training by US business schools. This paper identifies matching, under-supplying, and over-supplying knowledge areas. Under-supply in emerging areas such as SCM information technology and certain logistics management topics are found. Some traditional OM topics are over-supplied due to out-of-date industry applications and should be revised to reflect the field’s transition from an OM to SCM view. Last, this paper makes recommendations to different stakeholders in this matching supply with demand process.

This study contributes to the literature in two ways. First, it provides an up-to-date understanding on demand and supply of SCM talent in USA. Second, it provides insights and recommendations not only to educators on curriculum design, but also to potential candidates interested in SCM careers, to companies’ job recruiters, and to professional organizations (such as APICS and Council of Supply Chain Management Professionals) to reduce the gaps between demand and supply.

This paper aims to explore the determinants of firms’ attitudes to R&D cooperation with universities with the goal to propose a model of knowledge transfer university-firm.

By understanding university-industry cooperation as a process, the model in this study is based on two factors: the “promoter of university-industry relations” (PUIR) and a technological system that is able to match the research supply of universities with the demand for innovation of the firms. A total of 375 firms, 420 research groups and 18 experts in knowledge transfer from Spain, Portugal and France have been involved in this study.

This study provides the first evidences of the relation between the number of matches’ demand-supply of research, the number of university-firm relations and the willingness to cooperate; all of these presented in the knowledge transfer model. Results also reinforce the utility of the role of PUIR and the matching system in the open innovation process.

However, more evidences are necessary to get a complete validation of the model. In the future, the continuous utilization of the matching system by the PUIRs of the selected regions will allow the authors to evaluate how well the system is working by analysing the possible increases in formal collaboration university-firm in the area of R&D.

This study contributes to the literature through aligning the open innovation and intellectual capital theories in the specific and complex context of university-firm collaboration. Also, little research regarding this topic has been noticed in SUDOE European Union (EU) countries (SUDOE is an EU territorial cooperation programme that supports regional development by funding transnational projects).

In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer physically fitting products. Digital product fitting is an emerging operational practice in retail that uses digital models of products and customers to match product supply to customer requirements. This paper aims to explore potential supply chain outcomes of digitalizing the operational practice of product fitting. The purpose is to explore and propose the potential of the practice to improve responsiveness to customer requirements and the utilization of existing variety in mass-produced products.

A maturity model of product fitting is developed to specify three levels of digitalization and potential outcomes for each level. Potential outcomes are developed based on empirical data from a case survey of three technology-developing companies, 13 retail cases and a review of academic literature.

With increasing maturity of digital product fitting, the practice can be used for more purposes. Besides matching product supply to customer demand, the practice can improve material flows, customer relationship management, assortment planning and product development. The practice of digital product fitting is most relevant for products where the final product configuration is difficult to make to order, product and customer attributes are easily measurable and tacit knowledge of customers and products can be formalized using digital modeling.

Potential outcomes are conceptualized and proposed. Further research is needed to observe actual outcomes and understand the mechanisms for both proposed and surprising outcomes in specific contexts.

The maturity model helps companies assess how their operations can benefit from digital product fitting and the efforts required to achieve beneficial outcomes.

This paper is a first attempt to describe the potential outcomes of introducing digital product fitting in retail supply chains.

This paper aims to study the material conscious information network (MCIN) to present new models of clothing products and persons and propose new crowd-designing patterns to reconstruct an improved supply–demand relationship in clothing industry.

This paper aims to study the MCIN to present new models of clothing products and persons and propose new crowd-designing patterns to reconstruct an improved supply–demand relationship in clothing industry.

At last, this paper implements a prototype system of novel e-commerce platform based on the CDCI to illustrate the effectiveness and soundness of the CDCI modeling.

Different from most related works just focusing on the physiology dimension in the matching of customer and clothing, this paper proposes that the dimension of physiology, character, knowledge and experience should be synthetically considered.

Digital disruption offers an innovative opportunity to address housing affordability issues through the use of market design theory and two-sided matching markets. The purpose of this paper is to scope a model for how “uberisation” can revolutionise the traditional apartment delivery model in Australia, leading to improved housing affordability.

This paper uses semi-structured interviews with operators of online real estate platforms and deliberative developers to examine how the principles of “uberisation”, that is two-sided matching markets, are driving innovation in the apartment supply process.

Findings confirm that real estate internet platforms and deliberative developers innovators are informed by the benefits of aggregating demand to reduce development risk, thus enabling apartments to be provided at a substantially lower price than by traditional methods.

The number of interviews is small reflecting the limited number of market actors currently engaged in the innovations investigated.

This research is innovative as it introduces theoretical understandings gained from market design theory and applies those concepts to disrupt the apartment development process.

This study explores the factors that influence platform-to-platform cooperation (PPC) and designs a theoretical framework for platform research.

This multi-case study includes a combination of exploratory and explanatory case studies.

From the internal factor perspective, channel integration capability, technology-based order matching capability and service innovation capability positively affects the PPC. From the perspective of external factors, the impact of a new platform entry on the PPC depends on market power and complementarities between platforms in the supply and value chains. Diversity of demand also has a positive effect on the PPC, which is moderated by network externalities. It is worth noting that the incumbent platform prefers to diversify its services for collaborating platforms with a higher level of cooperation. In addition, the higher diversity of demand, the stronger the service innovation capability, which indirectly impacts cooperation positively.

The PPC has gained immense popularity in recent years. However, no scholars have investigated the factors influencing the PPC decisions, which warrants further exploration. This study sheds light on the factors and mechanisms that influence the PPC from both internal and external perspectives.

Risks can easily disrupt the demand–supply match targeted by sales and operations planning (S&OP). As surprisingly little is known of how organizations identify, assess, treat and monitor risks through tactical planning processes, this paper zooms in on the S&OP set-up and process parameters to explore how risks are managed through S&OP.

A multiple case study analyzes the S&OP processes of seven organizations in the process industry, drawing on 17 in-depth interviews with high-ranking representatives, internal and external documents, and a group meeting with participating organizations.

The study finds that organizations proactively design their S&OP based on their main risk focus stemming from the planning environment. In turn, such designs proactively support organizations' risk identification, assessment, treatment and monitoring through their S&OP execution. Reactively, a crisis S&OP meeting – making use of the structure of S&OP – can be used as a risk-treatment tool, and S&OP design can be temporarily adapted to deal with emerging risks.

This study is among the first to empirically elucidate risk management through S&OP. S&OP design, execution and adaption are identified as three interconnected strategies that allow organizations to manage risks. The design enables risk management activities in the monthly execution of S&OP. The reactive role of S&OP in risk management is particularly novel.

Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy.

A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry.

The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments.

So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.