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Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

This study aims to further develop the CLC stage/path’s identification model that distinguishes between path’s emergence (emergence stage), path’s development (growth stage), path’s sustainment (maturity stage), path’s decline (decline stage) and path’s transformation (renewal stage), and by applying it, define the current stage/path of the Demarcated Douro Region (DDR) cluster. The Port wine industry, which is the dominant industry of the DDR cluster, is at the maturity/decline stage – is the same for the cluster itself?

It is a case study with a longitudinal perspective based on the analysis of the dynamics of the parameters of cluster evolution using available secondary sources (cluster identity/brand; number of firms; number of employees; network; innovation; policies and regulations; and external markets – exports), especially addressing the past decade, that represent the stage of maturity/decline of the cluster’s dominant Port wine industry.

The conclusion is that since the 1990s the Demarcated Douro Region has gone through a “path transformation” where during the following 20 years new “anchors” for the cluster were gradually introduced, such as Doc Douro Wines, new forms of consumption of Port wine, tourism and olive oil. Since 2010 the cluster has entered a growth stage/(new) path’s development, where these “anchors” are in steady growth. The Douro brand is becoming more internationally recognized and established, the number of firms and employees is increasing, the network is restructuring with the creation of cluster-specific official institutions, innovation is especially reflected with increasing heterogeneity through diversification of the clusters into new activities and regulations and policies are supportive for expansion – all these parameters are indicating the rise of the new cycle for the cluster. Thus, the DDR cluster represents an attractive business environment and requires attention from regional policymakers to support the cluster’s development. Especially institutions have been highlighted as internal factors driving clusters growth, European integration as an external factor and firms’ strategies of diversification and internationalization as an appropriate de-locking mechanism for new path’s development.

This research contributes to the CLC theory by further developing and applying a CLC stage/path identification model. It provides a better understanding of the dynamics of the DDR cluster that diverge from its dominant industry life cycle, which is relevant for regional policies and firms’ strategies. This study has its limitations. It provides an exploratory application of the theoretical framework proposed, and consequently, no general conclusions are possible yet. More empirical studies with different clusters in different stages are necessary to test the framework.

These findings are useful to policymakers when designing their policies for cluster development but also for clusters’ entities and actors when making their strategic decisions as it allows based on the verification of the established parameter of CLC to identify its current stage/path of development.

The paper presents a theoretically grounded model for CLC identification and for the first time to the best of the authors’ knowledge applies it to a cluster case – the DDR cluster. This case applies the proposed model and illustrates its usefulness. The model provides the tools for a better understanding of cluster dynamics.

This paper aims to investigate the interrelated role of relational commitment and dependence as drivers of key performance outcomes. Specifically, the authors provide a conceptual model of the impact of commitment on relationship value dependence and switching cost dependence. The authors further investigate how these dimensions of dependence offer differing noneconomic and economic paths to strategic and financial performance.

Survey data was collected from 296 purchasing agents across multiple industries located in the USA. The conceptual model and accompanying hypotheses were tested via partial least squares structural equation modeling.

The results show that the relational path is driven by affective and normative commitment, which are related to relationship value dependence. Conversely, calculative commitment is related to switching cost dependence. This economic path is related to both strategic and financial performance, whereas the relational path is more closely related to strategic as opposed to financial performance outcomes.

This study extends research on Business-To-Business (B2B) relationships by leveraging social exchange theory to examine the interrelated roles played by two forms of dependence on performance outcomes. Thus, the authors answer Scheer et al.’s (2015) call for research into the two distinct types of dependence – relationship value and switching cost dependence – and their roles in determining B2B relationship outcomes. The findings contribute to the literature by integrating social exchange and relationship marketing concepts to develop a dual pathway approach to B2B partnerships.

The results suggest that dependence is not necessarily negative for firms. Specifically, buyers can and do still exhibit positive performance, both strategic and financial, in relationships with suppliers even when dependent on the relationship. Regardless of whether buyers are dependent due to a relationship or economic factors, both can, in different ways, lead to positive strategic and financial outcomes. Together, the authors contribute to the understanding of B2B partnerships by offering guidelines for both buyers and suppliers in the dyad.

The authors derive a comprehensive model depicting primarily relational and economic paths to performance through different types of commitment and dependence. The authors contribute to the literature by demonstrating that relational and economic paths to success are not the same, highlighting how firms could influence performance even when the relationship is not necessarily characterized by generally positive relational benefits and behaviors.

Researchers explore the impact of an intelligent assistant in virtual teams by applying the theoretical lens of a transactive memory system (TMS) to understand the relationships between trust in a specific technology, knowledge sharing and knowledge application.

An online survey was administered to a Qualtrics-curated panel of individual, US-based virtual team members utilizing an intelligent assistant with team collaboration software. Partial least squares structural equation modeling (PLS-SEM) was utilized to examine the hypothesized relationships of interest.

Results suggest that knowledge application is strongly influenced by trust in a specific technology and knowledge sharing. Additionally, a transactive memory system positively increases trust in the intelligent assistant, and similarly, trust in the intelligent assistant has a significant positive relationship with knowledge sharing.

The research model contributes to our understanding of the impact of an intelligent assistant in virtual teams. Although the transactive memory system construct has been explored in various contexts and models, few have explored the impact of an intelligent assistant and trust in a specific technology.

The purpose of this paper is to study the effect of corporate social responsibility (CSR) effectiveness on job seekers’ organizational attractiveness (JSA). Deriving inspiration from Carroll’s theory, the study specifically tries to measure the impact of CSR on JSA with the four dimensions pertaining to economics (PECO), pertaining to legal compliance (PLCO), pertaining to ethics (PETH) and pertaining to philanthropic (PPH). Furthermore, the paper also tries to examine the moderating role of company selection (COM SEL) done based on high or low CSR reputation and JSA.

Management and engineering students enrolled in premier institutions and universities of Western Odisha in India are surveyed for their perceptions of CSR and JSA. Purposive and convenience sampling are applied to collect data from 456 job seekers. Based on the analysis, the study proposes two main models (Models 1 and 2) wherein Model 1 tries to measure the effect of CSR on JSA and Model 2 checks the moderating effect of COM SEL on CSR and JSA. In addition, robustness of the study is tested using control variables (Models 3 and 4). Data is treated through SmartPLS 3.3 software. The structural equation modelling (partial least squares-SEM) method is applied to test the hypotheses and for further analysis.

The result reveals an interesting insight. There is a positive and significant effect of PECO, PLCO and PPH on JSA. Moreover, no such significant effect is observed between PETH and JSA. Further, the findings are contrary with respect to COM SEL, that partially moderates the effect of CSR on JSA. However, the results reveal that COM SEL has a substantial moderating effect on the PPH dimension of CSR and JSA.

The results highlight that CSR positively and significantly affects JSA in terms of PECO, PLCO and PPH, thereby emphasizing that organizations must be more focused on these perspectives of CSR. Further, though the results did not exhibit any significance with PETH, it is essential that organizations should strengthen the ethical aspects of CSR as well and align them with the CSR strategic actions. The study also confirms the moderating effect of COM SEL on PPH dimension of CSR and JSA, thereby supporting the philanthropic approach in this domain. Further, the organizations should foresee the philanthropic factor of CSR as a competitive advantage to attract potential job seekers.

This research attempts to contribute to CSR and HR literature in two ways. First, it is the first attempt to use PLS-SEM with an attempt to understand job seekers’ perception of CSR and JSA with Indian data consisting of students belonging to premier business management and engineering institutes. Second, the study is an attempt to empirically measure the moderating effect of COM SEL on JSA. To sum up, the study will provide insights to organizations to help craft CSR strategies for attracting more job seekers.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

To co-create value through dynamic collaborations, enterprises and their suppliers need to orchestrate the integration of complementary resources when providing business services. As such enterprises' strategic decision to apply a plural sourcing strategy to establish dynamic collaborations with their suppliers implies that both in-house and outsourced business services should be bundled into a business services portfolio. However, the antecedents that affect a business services portfolio have rarely conceptually been identified.

Drawing on resource orchestration theory, the authors theoretically developed and empirically validated a business services portfolio conceptual model. The model aims at explaining the critical antecedents to a business services portfolio based on a unique data set, comprising 121 international enterprises with variation in the degree of outsourcing, size, geographies and maturity. Partial least squares structural equation modelling was used to examine the relationships among the antecedents and a business services portfolio.

The findings show that the antecedent, i.e. plural sourcing strategy, modularised business processes and customer orientation have a direct and significant effect on the enterprises' business services portfolio orchestration. The results of the mediation test indicate that modularised business processes fully mediate the relationships between three independent variables with the orchestration of business services portfolio.

This study is the first to analyse the impact of plural sourcing strategy, modularised business processes and customer orientation on the business services portfolio orchestration from a plural sourcing context. Additionally, it examines the mediating role of modularised business processes in the relationship between the antecedents and business services portfolio orchestration.

This study aims to investigate how social entrepreneurial role models influence social entrepreneurial self-efficacy, social entrepreneurial intent and social entrepreneurial action, with moral obligation as a moderator.

A cross-sectional survey of 261 pupils in the South African province of the Eastern Cape was used in the research study. Structural equation modeling was used to test hypotheses.

The research revealed that having social entrepreneurial role models has a positive impact on both social entrepreneurial self-efficacy and social entrepreneurial intent. In addition, a connection was found between social entrepreneurial intent and entrepreneurial action. The influence of moral obligation was found to be a positive and a significant moderator. Moreover, the association between social entrepreneurial role models and social entrepreneurial intent was mediated by social entrepreneurial self-efficacy.

The findings are not generalizable to nonstudent samples because students constituted the sample for gathering data. Future study therefore requires considering nonstudents to generalize the outcomes. This research should be replicated in other South African provinces and other developing countries for comparative outcomes.

Since social entrepreneurial role models have been practically linked to social entrepreneurship intent and entrepreneurial efficacy, understanding the factors that influence student’s decision to start a social enterprise is critical in South Africa to develop targeted interventions aimed at encouraging young people to start new businesses. Policymakers, society and entrepreneurial education will all benefit from the findings.

This study contributes to bridging the knowledge gap as it investigates how social entrepreneurial role models influence social entrepreneurial self-efficacy, social entrepreneurial intent and social entrepreneurial action, with moral obligation as a moderator. Encouraging social entrepreneurship among South African youth would also help address societal issues. This is a pioneering study in the context of an emerging economy such as South Africa, where social entrepreneurship is so integral.

This article explores whether six broad categories of activities undertaken by Canadian business scholars’ academics: publications record, citations record, teaching load, administrative load, consulting activities, and knowledge spillovers transfer, are complementary, substitute, or independent, as well as the conditions under which complementarities, substitution and independence among these activities are likely to occur.

A multivariate probit model is estimated to take into account that business scholars have to consider simultaneously whether or not to undertake many different academic activities. Metrics from Google Scholar of scholars from 35 Canadian business schools, augmented by a survey data on factors explaining the productivity and impact performances of these faculty members, are used to explain the heterogeneities between the determinants of these activities.

Overall, the results reveal that there are complementarities between publications and citations, publications and knowledge spillovers transfer, citations and consulting, and between consulting and knowledge spillovers transfer. The results also suggest that there are substitution effects between publications and teaching, publications and administrative load, citations and teaching load, and teaching load and administrative load. Moreover, results show that public and private funding, business schools’ reputation, scholar’s relational resources, and business school size are among the most influential variables on the scholar’s portfolio of activities.

This study considers simultaneously the scholar’s whole portfolio of activities. Moreover, the determinants considered in this study to explain scholars’ engagement in different activities reconcile two conflicting perspectives: (1) the traditional self-managed approach of academics, and (2) the outcomes-focused approach of university management.