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This research aimed to measure the influence of resource dependency (trading and/or shareholder's dependencies) squad age structure by building archetypes to identify strategic dominant schemes.

Based on the Ligue 1 football clubs from the 2009/2010 season to the 2018/2019 data, the authors use the k-means classification to build archetypes of resource dependency and squad structure variables. The influence of resource dependency on squad structure is then analysed through a table of contingency.

Firstly, the authors identify archetypes of resource dependency with some clubs that are dependent on the transfer market and others that do not count on sales to balance their account. Secondly, they provide different archetypes of squad structure choices. The contingency between those archetypes allows to identify three main strategic schemes (avoidance, shaping and adaptation).

The research tests an original relationship between resource dependency of clubs and their human resource strategy to respond to it. This paper can help to provide detailed profiles for big clubs looking for affiliate clubs to know which clubs have efficient academy or player development capacities.

This study draws on the conservation of resources logic to theorize the role of firm resilience in explaining variations in entrepreneurial well-being under varying conditions of supply chain disruption and dependency ratio.

The study uses ex-post survey data from 373 women entrepreneurs in diverse agricultural supply chains in Ghana, a sub-Saharan African country. Moderated regression analysis is employed to test the research hypotheses.

The results indicate that firm resilience has both positive and negative relationships with economic and subjective well-being, depending on the level of supply chain disruption and dependency ratio women entrepreneurs face. Notably, the findings suggest that firm resilience contributes more to economic and subjective well-being of women entrepreneurs when dependency ratio is low and supply chain disruption is high.

The study integrates firm resilience research and entrepreneurial well-being literature to provide new insights into theorizing and analyzing the benefit of firm resilience for women entrepreneurs’ well-being.

The purpose of this paper is to propose a framework for evaluating the relationship between China and Peru, drawing on dependency theory, against the backdrop of China’s explicit policies towards foreign direct investment. It seeks to transcend traditional interpretations of this relationship in the literature that focuses on China as either hegemon or a South–South partner to Latin American countries to highlight a more nuanced relationship.

The paper adopts a case study approach, focusing on China in Peru. The authors examine three areas of traditional, strategic and emerging industries drawing from Chinese national policies, reviewing these against characteristics of dependency: control of production, heterogeneity of actors, transfer of knowledge and delinking.

The authors find that Chinese foreign direct investment (FDI) in Peru demonstrates mixed motives and collectively operates as an ambiguous player. Chinese firms appear to be willing to work with various actors, but this engagement does not translate into a decolonial development alternative in the absence of a Peruvian political will to delink and Chinese willingness to actively transfer control of production and knowledge.

This paper contributes to existing literature on China in Latin America by evaluating Chinese outward FDI in Peru against China’s strategic aims in terms of a re-evaluation of dependency theory.

This paper explains why some firms manage to capture disproportionate value from their network of relationships, leading to superior performance. The paper examines how a firm's dependencies affect its value appropriation potential (VAP) in economic networks.

The paper follows the axiomatic method and the embeddedness perspective of firms to develop an index called nodal power, which captures the power that accrues to a firm in exchange-based economic networks. Thereafter, using the formal method and simulation, it shows nodal power reflects a firm's VAP in economic networks.

The study analysis and findings prove that a firm's dyadic level exchange relations and the embedded network structure determine its VAP by affecting the nodal power. A firm with lesser nodal power is likely to appropriate less value from its relations even if it equally contributes to the value creation. This finding explains how the structural and relational characteristics of a firm's network enable disproportionate value appropriation.

Nodal power furthers the scope of analyzing firms' economic relationships and changing power equations in dynamic networks. It can help firms build optimal strategic networks and manage the portfolio of relationships by predicting the impact of changing relations on firms' VAP.

The paper's original contribution is to explain, through formal analysis, why and how the structure and nature of relations of firms affect their VAP. The paper also formalizes the power-dependence principle through a dependency-based index called nodal power and uses it to show how interfirm dependencies are key to value appropriation.

The purpose of this study was to investigate the affective and cognitive dimensions of satisfaction that impact the buyer-supplier relationship (BSR) from the supplier's perspective and to consider satisfaction within the context of power-dependency theory.

Qualitative in-depth interviews were conducted with 20 Chinese apparel supply professionals who regularly interact with apparel buyers. Audio or video interviews were conducted via WeChat (the most popular social media platform in China).

A thematic analysis of the interview data revealed that both affective and cognitive dimensions of satisfaction impact the BSR. A model of supplier affective and cognitive satisfaction in a collaborative BSR was developed to illustrate the connections between the two dimensions.

Due to intense competition in the market, supplier satisfaction is essential for building relationships in the apparel industry. Existing studies have focused on satisfaction from the perspective of the buyer rather than the supplier because in a BSR, the buyer tends to hold more power. Moreover, research has primarily considered cognitive evaluations of satisfaction with the BSR. This study offers new insight on both cognitive and affective satisfaction from the perspective of suppliers within the context of power-dependency theory.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

The Logistics Performance Index (LPI), published by the World Bank, is a key measure of national-level logistics performance. It comprises six indicators: customs, infrastructure, international shipments, service quality, timeliness, and tracking and tracing. The objective of this study is to explore temporal dependencies among the six LPI indicators while operationalizing the World Bank’s LPI framework in terms of mapping the input indicators (customs, infrastructure, and service quality) to the outcome indicators (international shipments representing cost, timeliness, and tracking and tracing representing reliability).

A Bayesian Belief Network (BBN)-based methodology was adopted to effectively map temporal dependencies among variables in a probabilistic network setting. Using forward and backward propagation features of BBN inferencing, critical variables were also identified. A BBN model was developed using the World Bank’s LPI datasets for 2010, 2012, 2014, 2016, 2018, and 2023, covering the six LPI indicators for 118 countries.

The prediction accuracy of the model is 88.1%. Strong dependencies are found across the six LPI indicators over time. The forward propagation analysis of the model reveals that “logistics competence and quality” is the most critical input indicator that can influence all three outcome indicators over time. The backward propagation analysis indicates that “customs” is the most critical indicator for improving the performance on the “international shipments” indicator, whereas “logistics competence and quality” can significantly improve the performance on the “timeliness” and “tracking and tracing” indicators. The sensitivity analysis of the model reveals that “logistics competence and quality” and “infrastructure” are the key indicators that can influence the results across the three outcome indicators. These findings provide useful insights to researchers regarding the importance of exploring the temporal modeling of dependencies among the LPI indicators. Moreover, policymakers can use these findings to help their countries target specific input indicators to improve country-level logistics performance.

This paper contributes to the literature on logistics management by exploring the temporal dependencies among the six LPI indicators for 118 countries over the last 14 years. Moreover, this paper proposes and operationalizes a data-driven BBN modeling approach in this unique context.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

This study aims to posit the extended version of the technology acceptance model to explain the psychological factors of Muslim university students’ smartphone addictive behaviour and neglect of studies. In particular, this research conceptualised addictive behaviour as smartphone dependency and proposed process usage, flow, habitual smartphone behaviour and preference for online social interaction (POSI) as the associated factors. Besides, this study investigated the effect of smartphone dependency on the neglect of studies.

Following the quantitative research design, this research tested the proposed model using data collected from Muslim students’ smartphone users in Malaysia. The analyses were based on the partial least squares–structural equation modelling technique.

The results revealed that flow has the most significant influence on smartphone dependency. Habitual behaviour and process usage were also significant predictors, but POSI was not significantly associated with smartphone dependency. Also, smartphone dependency was significantly related to the neglect of studies.

This study provides empirical support to guide university management in preventing Muslim students’ over-dependence on smartphones. The Muslim community is synonymous with time management, where overdependence would diminish students’ religious and academic responsibilities. Such overdependence can cause neglect of studies; thus, the research findings can assist the university in introducing policies and campaigns that can educate students on the negative consequences of this pervasive digital addiction and ways to overcome them. This study contributes to the body of knowledge in understanding Muslim students coping mechanisms through smartphone usage.

This study contributes to the literature by demonstrating the psychological factors of smartphone addiction and the resultant effect on the neglect of studies among Muslim university students.

The present research aims to empirically test the “Barriers to abusive supervision model” to find how employee-related (core self-evaluations) and situational factors (perceived job dependency) make an employee trapped in the spiral of supervisory abuse. In addition, the work–family spillover lens is used to explain how employees' retaliation is targeted at their families in response to abuse from their bosses.

The current study has employed a three-wave longitudinal moderated mediation design and analysed data from 265 employees working in the hospitality industry of Pakistan.

The results of this study have shown that low core-self evaluations put employees in a spiral of supervisory abuse and they instil aggression towards their families. This association is further strengthened when employees are dependent on their job.

This study is one of the first to use the “Barriers to Abusive supervision” model to answer who and in which conditions tend to trap in the spiral of abuse and integrate the work-to-family interface model for elaborating the outcomes to the family domain.