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The aim of this research is to explore how a programmatic multi-project context influences project-based firms (PBFs) in organizing their relations with other PBFs and suppliers in a project-based industry.

A multiple case study research is conducted. Data are collected from two case studies in the construction infrastructure sector. Eleven interviews with contractors and other suppliers are the primary source of data collection. The data are complemented by procurement documents and expert consultations.

The findings show that within a programmatic multi-project context, PBFs settle relations with (1) key partners for program management capacity, PBFs establish relations with (2) main contractors to divide projects and (innovation) tasks, and PBFs intensify relations with (3) suppliers to ensure continuity and expertise.

The study contributes to the body of project management literature by exploring PBF’s relations with other PBFs and suppliers in a multi-project context. Based on empirical data, the study provides a distinction in layers presenting distinct levels of PBF’s supplier relations. This layer structure provides an excellent starting point for future studies exploring the program perspective of PBFs in the integrated supply chain.

Given the increase in programmatic multi-project context for project-based domains, discussed in both literature and practice, this study explores the effect of programs on relations of PBFs with other PBFs and suppliers. The study distinguishes PBF’s relations with the different suppliers in three layers and discusses the characteristics of these relations.

The view that dynamic capabilities theory can help explain how lean organizations improve has been put forward by scholars. However, there is still a lack of research on the matching relationship between the application of lean practice and the internal elements of enterprise organization from the perspective of dynamic capabilities. The purpose of this study is to validate the moderating effect of dynamic capabilities on the relationship between lean practices and operational performance.

This study used the method of survey and empirical research to collect sample data from 263 enterprises in China. Through literature review, this study put forward the moderating hypotheses around dynamic capabilities, lean practices and operation performance and used the method of regression analysis to validate these hypotheses.

The results showed that dynamic capabilities have a partially moderating effect on the application of lean practices. Specifically, dynamic capabilities have a significant moderating effect on the relationship between just-in-time, total quality management, total preventive maintenance and operational performance, while dynamic capabilities have no significant moderating effect on the relationship between human resource management and operational performance.

The research conclusion complements and enriches the lean practices literature from the perspective of dynamic capabilities. Existing studies mainly focus on the moderating role of external environmental factors, while there is a lack of empirical research on the role of dynamic capabilities in lean practices literature. The research results will help enterprises further understand the matching relationship between lean practices and dynamic capabilities and then improve the success of lean practices application.

Given the sudden increase in plant investments across the manufacturing sector due to recent disruptions in the global supply chain caused by the COVID pandemic, the Ukraine/Russian war, ocean piracy incidents, and others, there is a need to strategically align the investments decisions with the overall business strategy. This study aims to establish whether strategic alignment enhances the effect of plant investments on operational performance.

Using the fifth version of the Global Manufacturing Research Group (GMRG V) dataset, we employ a structural equation model (SEM) to establish the moderating role of strategic alignment in plant investments-operational performance relationships.

The results suggest that strategic alignment enhances the positive effect of plant investments on operational performance, especially in cost, delivery, flexibility, and innovation. However, we found partial support for the moderating role of strategic alignment on quality performance.

Although the study was based on the GMRG global dataset and numerous papers have been published using the same dataset, the use of a convenience sample on a select group of companies may limit the generalizability of the results. With the increase in new plant investments in computer chip manufacturing, electronics, and electrical in recent years, the findings could help corporate executives maximize the benefits of their investment decisions. Companies should ensure that their plant investment decisions are strategically aligned with their overall business strategy to achieve optimal outcomes.

With the increase in new plant investments in computer chip manufacturing, electronics, and electrical in recent years, the findings could help corporate executives maximize the benefits of their investment decisions. Companies should ensure that their plant investment decisions are strategically aligned with their overall business strategy to achieve optimal outcomes.

The study comprehensively demonstrates that the extent of improvement in operational performance depends on how closely plant investment decisions align with the overall company strategy. Manufacturers should align major investment decisions with competitive priorities driven by market requirements to enhance operational performance.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

This study aims to model and investigate Basic Yellow 28 (BY28) adsorption onto activated carbon in batch and continuous process.

Batch adsorption experiments were carried out at 25 °C with 50 mg/L BY28 solution at pH 6 with different amounts of activated carbon. Freundlich and Langmuir adsorption isotherm models were used to model batch data. Pseudo-first-order and pseudo-second-order kinetic models were applied with linear regression. The changes of the breakthrough curve with the column height, flow rate, column diameter and adsorbent amount were examined in fixed bed column at room temperature. BY28 adsorption data were modelled by using different adsorption column models (Adams & Bohart, Thomas, Yoon & Nelson, Clark and modified dose–response) with non-linear regression.

Freundlich model and pseudo-second-order kinetic model expressed the experimental data with high compatibility. Modified dose-response model corresponded to the fixed bed column data very well.

Adsorption of Basic Yellow 28 on activated carbon in a fixed bed column was studied for the first time. Continuous adsorption process was modelled with theoretical adsorption models using non-linear regression.

Point-of-interest (POI) recommendation techniques play a crucial role in mitigating information overload and delivering tailored services. To address limitations in conventional POI recommendation systems, constrained by sparse user-POI interactions and incomplete consideration of temporal dynamics, POI recommendation based on the spatial-temporal graph (STG-POI) is proposed.

Spatial-temporal sequence graphs from geographical locations and user interaction history data are constructed, which are used to mine spatial-temporal sequence information. Using the data filtered by the band-pass filter, graph neural networks with distance-awareness and sequence-awareness are applied to capture high-order spatial-temporal connections within diverse graph topologies. The model leverages contrastive learning for self-supervised disentanglement of graph representations, providing self-supervised signals for sequential and geographical intent perception, thereby achieving more precise POI personalization.

Compared to the baseline model GSTN, experiments on the Foursquare and Gowalla data sets reveal that STG-POI improves testing AUC by 2.0%, 2.1%, 2.0% and decreases logloss by 1.9%, 3.3%, 0.3%, respectively. These results indicate the model’s effectiveness in capturing spatial-temporal information, surpassing mainstream POI recommendation baseline models.

This approach constructs a dual graph from user interaction data, harnessing sequential and geographical information as self-supervised signals. It yields decoupled representations of these influences, offering a comprehensive insight into user behaviors and preferences within location-based social networks, thus enhancing recommendation accuracy and interpretability. This approach addresses the challenge in graph convolutional network where only rough and smooth features are conducive to recommendation by using band-pass filters to significantly reduce computational complexity, thereby enhancing recommendation speed by filtering out noise data that does not contribute to recommendation performance. Experimental results indicate that this model surpasses current mainstream approaches in POI recommendation tasks, effectively integrating both geographical and temporal features.

This study aims to investigate the role of green strategic alliances (GSAs) in fostering a green shared vision (GSVis) and green shared value (GSV) and their impact on green organizational identity (GOI) and sustainability.

The authors employed structural equation modeling to analyze data collected through a 60-item questionnaire administered in Portugal and China, allowing the authors to test their theoretical model.

The findings of the authors' study indicate that green strategic alliances have a positive influence on the development of a GSVis and GSV in both countries. This, in turn, contributes to improved sustainability and the establishment of a GOI. Furthermore, the authors' results demonstrate that these alliances enhance GSV, resulting in enhanced sustainability performance and a stronger green identity, with a notable increase in awareness of environmental and social practices.

This article is innovative as it applies organizational learning and value creation theories to gain a deeper understanding of how alliances can shape the green identity of companies and contribute to their overall sustainability.

The construction industry has experienced an irreversible digital transformation to smart construction. Many countries have published supporting policies to encourage the development of smart construction. However, there is no universally valid approach. This paper thus aims to evaluate smart construction policies issued by 24 pilot cities in China and identify applicable policy tools and their impact.

This paper collected 33 governmental documents on smart construction through the official websites in China. Different policy tools were classified into supply-side, demand-side and environment-side categories. The supporting policies of smart construction development in pilot cities were quantitatively evaluated by using a policy modeling consistency index (PMC-index) model.

Supply-type and environment-type policy instruments were used more frequently than demand-type policies in 24 pilot cities. Most of the 24 pilot cities had an evaluation of PMC-index over 8, realizing the consistency of smart construction policies. Eight pilot cities had an evaluation of PMC-index of 6–7.99, realizing acceptable consistency. Only Foshan City has an evaluation of PMC-index below 4, which may reflect a poor consistency of policy implementation. The paper proposes consistencies of smart construction policies of 24 pilot cities and valid policy instruments, including the presale of commercial residential buildings, additional bonus points in the tendering process and cooperating with multiple departments when promoting smart construction.

This paper contributes to expanding policy evaluation studies in the smart construction field and provides concrete suggestions for policymakers to formulate more effective and specific policies and strategies for the development of smart construction.

Researchers have examined the influence of the factors on reducing return rates in retailing over the years. However, the returns experience is often an overlooked way to drive customer engagement and repeat sales in the now ubiquitous omnichannel setting. The focus on returns prevention in existing research overshadows management’s need to understand better the comprehensive mechanics linking the customer in-store return experience with their repurchase actions. Recognizing the need to bridge different stages of the returns management process, this research aims to explore the facilitators and barriers of in-store return activities.

Analysis of customer corporate data from 5,339 returns at the retail level provides insights from the customer return experience. Expanding our theoretical understanding, a deductive research approach then examines how those factors impact customer repurchase intentions both online and at brick-and-mortar stores. Stage two of the study employs a scenario-based role-playing experiment with consumer respondents to test hypotheses derived from signaling theory and justice theory.

Results find that returns policy and loyalty program capabilities are essential in creating a positive customer in-store experience. Moreover, a return experience enhanced by frontline employee service can retain existing shoppers and drive additional store traffic, further stimulating retailer sales.

These findings refine our understanding of returns management in evolving omnichannel retailing and offer practical insights for retailers to manage customer relationships through in-store returns.

How does business model design play a role in enabling manufacturing firms’ services? This study aims to investigate the impact of two distinct types of business model design, namely, efficiency-centered business model design (EBMD) and novelty-centered business model design (NBMD), and their effects in balanced and imbalanced configurations, on two types of services: product- and customer-oriented services.

Using matched survey data of 390 top managers and objective performance data of 195 Chinese manufacturing firms, this study uses hierarchical regression, polynomial regression and response surface analysis to test the hypotheses.

The results show that while EBMD positively affects product-oriented services, NBMD positively affects customer-oriented services. Both types of services exert a significant influence on firm performance. Furthermore, the degree of product- and customer-oriented services increases with an increasing effort level with a balance between EBMD and NBMD. Asymmetrical, imbalanced configuration effects reveal that the degree of product-oriented services is higher when the EBMD effort exceeds the NBMD effort, and the degree of customer-oriented services is higher when the NBMD effort exceeds the EBMD effort.

This study enriches the understanding of designing business models to facilitate service growth in manufacturing firms, ultimately benefiting firm performance. In addition, exploring balanced and imbalanced configurations of EBMD and NBMD offers new insights into business model dual design research.