Search results

Sociologists have long thought of the integration of people in communities – social integration – and hierarchical social systems – systemic integration – as contradictory goals. What strategies allow organizations to reconcile social and systemic integration? We examine this question through 40 in-depth, longitudinal interviews with leaders of nonprofit organizations that engage in the dual pursuit of social and systemic integration. Two processes reveal how the internal structure of organizations often mirrors the ways in which organizations are embedded in their local environments. When organizations engage in loose demographic coupling, relegating those who “match” the community to the work of social integration, they produce internal inequalities and justify them by claiming community building as sacred work. When engaging in community anchoring, organizations challenge internal and external inequalities simultaneously, but this process comes with costs. Our findings contribute to a constructivist understanding of community, the mechanisms by which organizations produce inequalities, and a place-based conception of organizations as embedded in community.

This paper aims to examine the financial outcome of reverse logistics among firms in a developing country. The authors draw on the organizational information processing theory to propose that analytics capability moderates the relationship between reverse logistics and financial performance.

The authors collected firm-level survey data from 200 manufacturing firms in Ghana, a developing country in sub-Saharan Africa. Partial least squares structural equations modeling is used to examine the proposed relationships, and the moderating effects are further probed using Hayes PROCESS.

The empirical results show that reverse logistics is negatively related to financial performance. However, analytics capability attenuates this negative relationship, such that firms with high analytics capability obtain a positive relationship between reverse logistics and financial performance.

Firms in developing countries should combine their reverse logistics strategies with developing analytics capabilities that help minimize uncertainties and increase the efficient collection and use of information to reduce the cost of reverse logistics.

This paper examines how reverse logistics relates to financial performance in low-resource contexts. Beyond the novelty of the context, it explores the information processing needs of reverse logistics systems and provides empirical data to support analytics capability. This has yet to be considered in prior studies.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.

This paper aims to investigate and explain the dual fracture behaviour of PA12 specimens sintered by selective laser sintering (SLS) as a function of wall thickness and build direction with a powder mixture 30:70. To achieve this objective, research related to chemical, thermal and structural behaviours as a function of the input variables was carried out to describe and explain why ductile-fragile behaviour occurs during fractures under uniaxial tension manufactured via a methodology of material analysis and manufacturing processes.

The factorial design 32 relates the fracture of PA12 tensile specimens to the horizontal, transverse and vertical build directions at 2.0, 2.5 and 3.0 mm thicknesses, respectively. Fractographic images revealed the fracture surfaces and their dual ductile-fragile behaviour related to the specimens’ measured crystalline, thermal, surface and chemical properties.

The study showed that thermal property variables differ depending on the input variables. The wall thickness variable affected this morphology the most, showing the highest percentage of the ductile area, followed by the transverse and vertical directions. It was determined that the failure in the vertical direction is due to crystalline gradients associated with the layer-by-layer construction process. The pore density may be closely related to generating ductile and brittle areas.

In this paper, fracture characterisation is performed based on the mechanical, chemical, structural, thermal and morphological properties of PA12 manufactured by SLS. In addition, a heatmap of porosities in cross-sections is constructed using a machine learning model (k-means) related to dual fracture behaviour. This research revealed significant differences in the fracture type according to the build direction. In addition, thin-section fractography provides a more detailed explanation of the fragile behaviour of the vertical direction associated with crystalline changes due to the direction of the sintering layers.

The study aims to examine consumers' intentions to visit wine tourism destinations (WTDs) in Canada post-COVID-19 by combining destination-related constructs with the theory of planned behavior (TPB).

Convenience sampling was employed in the online survey method to gather data. Using AMOS and SPSS software, structural equation modeling (SEM) was used to analyze the data.

The outcomes of the SEM show that a powerful model for predicting consumers’ intention to visit WTDs was developed by combining the TPB with additional variables. More precisely, the study identified that consumers' attitudes, perceived behavioral control, wine product involvement and motivation exhibit positive influences on their intention to visit WTDs. Conversely, subjective norms and the destination wine image did not influence the intention.

The findings have important ramifications for various parties involved, including the government, travel agencies, tourism associations and wine producers. This research's emphasis on consumer behavior enables practitioners to adjust to the changing needs of consumers in the post-pandemic environment.

The drawn-out model gives an improvised view of consumers’ behavioral intentions to visit WTDs post-COVID-19 by testing an integrated structural model comprising TPB and destination-related constructs. As far as the authors are aware, this research represents the first-ever effort to predict consumer's intentions to visit WTDs post-COVID-19.

Multi-unmanned aerial vehicle (UAV) systems have succeeded in gaining the attention of researchers in diversified fields, especially in the past decade, owing to their capability to operate in complex scenarios in a coordinated manner. Path planning for UAV swarms is a challenging task depending upon the environmental conditions, the limitations of fixed-wing UAVs and the swarm constraints. Multiple optimization techniques have been studied for path-planning problems. However, there are local optimum and convergence rate problems. This study aims to propose a multi-UAV cooperative path planning (CoPP) scheme with four-dimensional collision avoidance and simultaneous arrival time.

A new two-step optimization algorithm is developed based on multiple populations (MP) of disturbance-based modified grey-wolf optimizer (DMGWO). The optimization is performed based on the objective function subject to multi constraints, including collision avoidance, same minimum time of flight and threat and obstacle avoidance in the terrain while meeting the UAV constraints. Comparative simulations using two different algorithms are performed to authenticate the proposed DMGWO.

The critical features of the proposed MP-DMGWO-based CoPP algorithm are local optimum avoidance and rapid convergence of the solution, i.e. fewer iterations as compared to the comparative algorithms. The efficiency of the proposed method is evident from the comparative simulation results.

A new algorithm DMGWO is proposed for the CoPP problem of UAV swarm. The local best position of each wolf is used in addition to GWO. Besides, a disturbance is introduced in the best solutions for faster convergence and local optimum avoidance. The path optimization is performed based on a newly designed objective function that depends upon multiple constraints.

This study aims to investigate the favourable conditions that influence transgenerational value transmission (TVT), value acceptance and value similarity between generations in indigenous African business-owning families.

This study adopts a multiple case study design and draws on semi-structured face-to-face interviews to collect data from participants in seven indigenous Black business-owning families located in South Africa. The software ATLAS.ti was utilised to manage the data and reflexive thematic analysis was undertaken.

The analysis reveal four themes describing how transmission factors facilitate favourable conditions for successful TVT in IBSA business-owning families, namely, authoritarian parenting, a loving and connected family relational climate, the continuous reinforcement of autonomy during childhood development and family authenticity in the face of societies dominant values climate. Furthermore, value similarity is perceived to exist among the different family generations in the business-owning families.

This study is among the first to adopt the value acquisition model to empirically examine successful TVT and examine the extent of value similarity or dissimilarity, using the business-owning family as the unit of analysis. Novel contributions to family business literature and practices are proposing a model for TVT in an African context and studying relationships from a business-owning family perspective. The model for TVT could be used to socialise the NextGen members into value sets and behaviours that help business-owning families preserve their entrepreneurial legacy and family business longevity.

This study aims to analyze the effect of cognitive load and social value orientation on managers’ preferences when they face with two types of restructuring choices in financially distressed firms: the first belonging to the family of organizational restructuring (massive layoffs) and the second to the family of financial restructuring (debt increases).

The authors investigate experimentally the impact of managers’ cognitive load and social value orientation on the decision to restructure leveraged buyout (LBO) firms in financial distress by using either massive layoffs or debt increases.

By investigating the impact of managers’ cognitive load and social value orientation on the restructuring decision of an LBO firm in financial distress, the research reveals that, on average, cognitively loaded managers prefer massive layoffs over increased debt levels. The massive layoffs seemingly provide a relatively easier way to avoid conflict with influential, residual claimants. In contrast, social value–oriented managers actively avoid massive layoffs and prefer to increase debt.

These results imply that the performance mechanisms emphasized to improve agency relations, for example, in LBOs, have their own limitations during periods of financial distress. This study shows that one of these limits is related to cognitive distortions and personality traits.

In this research, the originality lies in understanding how managers’ internal factors affect their restructuring decision-making, in the case of LBO firms in financial distress.

Patriotism is often described as the “love of country” that individuals display in the acclamation of their national community. Despite the prominence of this sentiment in various societies around the world, organizational research on patriotism is largely absent. This omission is surprising because entrepreneurs, human resource (HR) divisions, and firms frequently embrace both patriotism and patriotic organizational practices. These procedures include (among other interventions) national symbol embracing, HR practices targeted toward military members and first responders, the adulation of patriots and celebration of patriotic events, and patriotic-oriented corporate social responsibility (CSR). Here, the authors argue that research on HR management and organization studies will likely be further enhanced with a deeper understanding of the national obligation that can spur employee productivity and loyalty. In an attempt to jumpstart the collective understanding of this phenomenon, the authors explore the antecedents of patriotic organizational practices, namely, the effects of founder orientation, employee dispersion, and firm strategy. It is suggested that HR practices such as these lead to a patriotic organizational image, which in turn impacts investor, customer, and employee responses. Notably, the effect of a patriotic organizational image on firm-related outcomes is largely contingent on how it fits with the patriotic views of other stakeholders, such as investors, customers, and employees. After outlining this model, the authors then present a thought experiment of how this model may appear in action. The authors then discuss ways the field can move forward in studying patriotism in HR management and organizational contexts by outlining several future directions that span multiple levels (i.e., micro and macro). Taken together, in this chapter, the authors introduce a conversation of something quite prevalent and largely unheeded – the patriotic organization.

With the development trend of China’s service-oriented manufacturing moving toward intelligence and personalization, the deep integration of manufacturing and service has become a synergistic challenge for enterprises.

An improved migratory bird optimization (IMBO) algorithm is proposed to solve the multiobjective FJSP model. First, this paper designs an integer encoding method based on job-machine. The algorithm adopts the greedy decoding method to obtain the optimal scheduling solution. Second, this paper combines three initialization rules to enhance the quality of the initial population. Third, three neighborhood search strategies are combined to improve the search capability and convergence of the solution space. Furthermore, the IMBO algorithm introduces the concepts of nondominated ranking and crowding degree to update the population better. Finally, the optimal solution is obtained after multiple iterations.

Through the simulation of 15 benchmark studies and a production example of a furniture enterprise, the IMBO algorithm is compared with three other algorithms: the improved particle swarm optimization algorithm, the global and local search with reinitialization-based genetic algorithm and the hybrid grey wolf optimization algorithm. The experiment results show the effectiveness of the IMBO algorithm in solving the multiobjective FJSP.

The study does not consider the influence of disturbance factors, such as emergency interventions and equipment failures, on scheduling in actual production processing. It is necessary to further study the dynamic FJSP problem.

The study proposes an IMBO algorithm to solve the multiobjective FJSP problem. It also uses three initialization rules to broaden the range of the solution space. The study applies multiple crossover strategies to avoid the algorithm falling into local optimality.