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This article aims to identify methodological openings and barriers for using managerial relevance as a method to further business to business (B2B) marketing theory.

There is currently an imbalance between two research modes; notably, the normative knowledge accumulation mode confers scant space to the solution mode. This negatively affects both the production of managerially relevant research and theoretical developments within B2B marketing which, in turn, hinders the development of marketing as an applied science. Against the background of an upgraded methodological cartography, emphasizing the equal importance of the two research modes, this article illustrates the context of using managerial relevance as a method to forward B2B marketing theory. An aggregate picture of corresponding calls in the literature shows why this is important.

Calls for increased managerial relevance and methodological diversity outside the normative knowledge accumulation mode will face difficulties in terms of changing researcher behavior, as long as the biased relationship between the two research modes persists. For managerially relevant research to increase, and for B2B marketing science to progress, underlying assumptions of different methodological stances have to be uncovered, calling for increased methodological clarity.

To prevent a relapse, the analysis suggests a further strategy by which to modify schematic models to study organizational change and behavior.

A more significant role attributed to managerial relevance for theory development early in the research process, permits to align the perspectives of research with the needs of practice and will decrease the research–practice gap.

The article pinpoints two research modes and three epistemological stances – predict, describe and depict – which sharpens methodological clarity beyond traditional qualitative–quantitative, and conceptual–empirical classifications.

This study aimed to overcome the challenging issues involved in providing high-precision eigensolutions. The accurate prediction of the buckling load bearing capacity under different crack damage locations, sizes and numbers, and analysing the influence mechanism of crack damage on buckling instability have become the needs of theoretical research and engineering practice. Accordingly, a finite element method was developed and applied to solve the elastic buckling load and buckling mode of curved beams with crack damage. However, the accuracy of the solution depends on the quality of mesh, and the solution inevitably introduces errors due to mesh. Therefore, the adaptive mesh refinement method can effectively optimise the mesh distribution and obtain high-precision solutions.

For the elastic buckling of circular curved beams with cracks, the section damage defect analogy scheme of a circular arc curved beam crack was established to simulate the crack size (depth), position and number. The h-version finite element mesh adaptive analysis method of the variable section Euler–Bernoulli beam was introduced to solve the elastic buckling problem of circular arc curved beams with crack damage. The optimised mesh and high-precision buckling load and buckling mode solutions satisfying the preset error tolerance were obtained.

The results of testing typical examples show that (1) the established section damage defect analogy scheme of circular arc curved beam crack can effectively realise the simulation of crack size (depth), position and number. The solution strictly satisfies the preset error tolerance; (2) the non-uniform mesh refinement in the algorithm can be adapted to solve the arbitrary order frequencies and modes of cracked cylindrical shells under the conditions of different ring wave numbers, crack positions and crack depths; and (3) the change in the buckling mode caused by crack damage is applicable to the study of elastic buckling under various curved beam angles and crack damage distribution conditions.

This study can provide a novel strategy for the adaptive mesh refinement for finite element analysis of elastic buckling of circular arc curved beams with crack damage. The adaptive mesh refinement method established in this study is fundamentally different from the conventional finite element method which employs the user experience to densify the meshes near the crack. It can automatically and flexibly generate a set of optimised local meshes by iteratively dividing the fine mesh near the crack, which can ensure the high accuracy of the buckling loads and modes. The micro-crack in curved beams is also characterised by weakening the cross-sectional stiffness to realise the characterisation of locations, depths and distributions of multiple crack damage, which can effectively analyse the disturbance behaviour of different forms of micro-cracks on the dynamic behaviour of beams.

Research in business processes has been developed around a disciplinary approach toward the production of traditional knowledge, known as Mode 1. The problems studied with this approach are solved in a context in which academic knowledge prevails, with no major concerns regarding its practical applicability. Thus, the purpose of this paper is to propose a structure for knowledge production based on Mode 2 for business process researches.

A bibliometric research was conducted to define and conceptualize the classes of disciplinary problems, by assessing the years 2007-2012 of the Business Process Management Journal publications.

A framework for the Mode 2 knowledge production was proposed in the development of research in business process and conceptualized classes of problems related to this issue.

This work was carried out with specific focus on research in business process, so the defined classes of problems cannot be generalized.

The studies identified by this research are in the form of a disciplinary approach toward the production of traditional knowledge, known as Mode 1. This paper aims to fill the gap of a transdisciplinary production of knowledge and practical application, known as Mode 2 in the context of business process.

This chapter reviews the literature on servitization to understand whether and how mergers and acquisitions (M&As) have been dealt with and what the portrayed consequences are of servitization through M&As. Servitization refers to how manufacturing firms extend and remodel their offerings to focus on value in use rather than product transfer. The rationale of the chapter follows from how business model innovation or business modeling has been predicted as the next M&A wave, while the focus on servitization has been pronounced in research and practice as a means for manufacturing firms to refocus operations during the past decade. The chapter concludes that while the servitization literature is vibrant, the mode of reaching service competence and renewing business is not well explored in the literature. In line with the predicted next M&A wave, servitization through M&As would thereby create an interesting path for future research.

A commonly suggested measure to make logistics greener is a shift to intermodal road‐rail transportation. Most research addresses the issue from the carrier's perspective, arguing for ways to improve the service production to better fit the shippers' demand. In this article the issue is addressed from the shipper's perspective. The purpose is to understand what contextual factors and operations changes that are possible and/or necessary for the shipper to make a fit to the current production system.

Six case companies selling non‐bulk, fast moving goods are examined. These firms have gone against the mainstream and shifted modes of transport. They are investigated through a multiple case‐study design.

The findings indicate that contextual factors stressed in the carrier‐focused literature, or rule of thumb decisions made by shipping logistics management, do not always clearly predict the success of a modal shift. However, some common denominators emerge among successful cases: large transport purchasing resources, high general carrier performance, low demand volatility, and centralized system control. The study also poses some propositions regarding the success of a modal shift.

The research is qualitative in nature and thus limited to the companies and their respective logistics systems. However, the models could be further evaluated empirically through quantitative and qualitative methods alike.

The paper poses a number of propositions of what constitutes a successful modal shift from a shipper's perspective, based on the identified factors and operational changes.

Previous research on the shift to intermodal road‐rail solutions are predominantly made from a carrier's perspective. This research addresses the issue from the shipper's perspective.

Cross-docking is a supply chain distribution and logistics strategy for which less-than-truckload shipments are consolidated into full-truckload shipments. Goods are stored up to a maximum of 24 hours in a cross-docking terminal. In this chapter, we build on the literature review by Ladier and Alpan (2016), who reviewed cross-docking research and conducted interviews with cross-docking managers to find research gaps and provide recommendations for future research. We conduct a systematic literature review, following the framework by Ladier and Alpan (2016), on cross-docking literature from 2015 up to 2020. We focus on papers that consider the intersection of research and industry, e.g., case studies or studies presenting real-world data. We investigate whether the research has changed according to the recommendations of Ladier and Alpan (2016). Additionally, we examine the adoption of Industry 4.0 practices in cross-docking research, e.g., related to features of the physical internet, the Internet of Things and cyber-physical systems in cross-docking methodologies or case studies. We conclude that only small adaptations have been done based on the recommendations of Ladier and Alpan (2016), but we see growing attention for Industry 4.0 concepts in cross-docking, especially for physical internet hubs.

The paper aims to study the challenges and solutions of city logistics in the new retail era. The new retail, which is characterized by omni-channel, fragmented orders and decentralized 2C distribution, is becoming the mainstream of the retail industry worldwide. In order to achieve a comprehensive breakthrough in new retail, the change of order fulfillment mode is the most noteworthy issue. The aim of this paper is to design a city logistics operation model for new retail and verify its feasibility and efficiency.

A physical internet (PI) enabled two-tier city logistics solution is proposed by redefining the key facilities in city logistics with the PI concept. A “Container-as-a-Warehouse” operation mode is designed to provide a more flexible store and transfer solution. A mathematical model of the proposed solution is established. An adaptive large neighborhood search (ALNS) is designed based upon an iterative procedure, which ensures consistent and optimal results.

To quantitatively assess the feasibility of the proposed solution, a computational experiment is designed to compare the performance of the proposed model against the conventional two-tier city logistics operation. The effects of geographical location pattern, utilization of PI-hub as well as the fluctuation of customer orders are analyzed. The results show that the PI-enabled city logistics is more advantageous than the conventional solution.

This study does not consider the impact of new technologies in city logistics; for example, the replenishment problem of unmanned vending machines and the charging problem of electric vehicles.

The proposed PI-enabled solution and analysis results in this paper have positive guiding significance for future practical application.

Based on the concept of PI, this paper proposes an innovative and practical operation model to solve the city logistics challenges.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Nowadays, the risk assessment and reliability engineering of various production processes have become an inevitable necessity. Because if these risks are not going to be evaluated and no solution is going to be taken for their prevention, managing them would be really hard and costly in case of their occurrence. The importance of this issue is much higher in producing healthcare products due to their quality's direct impact on the health of individuals and society.

One of the most common approaches of risk assessment is the failure mode and effects analysis (FMEA), which is facing some limitations in practice. In this research, a new generalized multi-attribute failure mode analysis approach has been proposed by utilizing the best–worst method and linguistic 2-tuple representation in order to evaluate the production process of hemodialysis solution in a case of Tehran, Iran.

According to the results, entry of waste to the mixing tanker, impurity of raw materials and ingredients and fracture of the mixer screw have been identified as the most important potential failures. At last, the results of this research have been compared with the previous studies.

Some reinforcement attributes have been added to the traditional FMEA attributes in order to improve the results. Also, the problems of identical weights for attributes, inaccuracy in experts' opinions and the uncertainties in prioritizing the potential failures were improved. Furthermore, in addition to the need for less comparative data, the proposed approach is more accurate and comprehensive in its results.

The purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.

A case study approach identified the activity execution modes in building construction projects in India to support multi-mode resource-constrained project scheduling. The data required to compute time, cost and quality of each activity are compiled from real construction projects. A binary integer-programming model has been developed to perform multi-objective optimization and identify Pareto optimal solutions. The RR-PARETO3 algorithm was used to identify the best compromise trade-off solutions. The effectiveness of the proposed framework is demonstrated through sample case study projects.

Results show that good compromise solutions are obtained through multi-objective optimization of time, cost and quality.

Case study data sets were collected only from eight building construction projects in India.

It is feasible to adopt multi-objective optimization in practical construction projects using time, cost and quality as the objectives; Pareto surfaces help to quantify relationships among time, cost and quality. It is shown that cost can be reduced by increasing the duration, and quality can be improved only by increasing the cost.

The use of different activity execution modes compiled from multiple projects in optimization is illustrated, and good compromise solutions for the multi-mode resource-constrained project scheduling problems using multi-objective optimization are identified.