Smart working as an organisational process or as a social change? An Italian pandemic experience

Purpose: Along the coronavirus pandemic, huge business challenges are facing as a result of collapsing customer demand and organisational significant changes supported by digital development, while the increasing social and environmental needs involve business and individuals. The authors argue that this trend is modifying organisational and market logic, replacing them with values and practices linked to community-based models. The present work aims to study the impact that smart working (SW) has on the worker, seen both as a member of the organisation and the social community. Design/methodology/approach: The study data were collected from a computer-assisted web interview administered in 2020 to public employees working for health agencies across the Campania region, in South Italy. To test the conceptual model, partial least squares-structural equation modelling is used. Considering the abductive soul of the research, the study represents a pilot survey that will deliver stochastic results to be subsequently replicated in all Italian health agencies. Findings: The results of the research highlighted how the evolutionary dynamics of SW employees tend towards a reconceptualisation of workspaces, a redefinition of time and emotions and a better balance between work and personal life, thus creating a greater space for social and community aspects and determining a greater involvement in their working life. Originality/value: This research introduces a new win-win logic in the labour market, one capable of generating advantages for people, organisations and the entire social system by allowing workers to better reconcile working times with their personal needs and with flexibility demands coming from companies. © 2023, Emerald Publishing Limited.

Is Public–Private Partnership (PPP) a Preferred Strategy for Procuring Smart Infrastructure in Developed Countries: An Empirical Study of the Perceived Benefits, Barriers and Recommended Strategies

Attention to Smart Infrastructure (SI) has risen due to its advantages, including better access, increased quality of life, and simplified maintenance management. To develop SI, Public–Private Partnerships (PPPs) are identified as potentially beneficial procurement strategies, which boost capacities to manage risks by pooling diverse resources. However, the applicability of PPP in SI developments in developed countries is scarcely researched. This may be due to underestimating the other potential benefits from PPP, although developed countries may have their own funding to develop SI. Hence, this research aims to evaluate the significant factors influencing the success of PPP in SI projects in developed countries based on public-sector satisfaction (S1), private-sector satisfaction (S2), and end-user satisfaction (S3). A comprehensive literature review was followed by expert interviews and an international survey, focusing on developed countries. The Partial Least Squares Structural Equation Modeling (PLS-SEM) technique was applied to map the connections amongst the influencing factors and S1, S2, and S3. The results reveal that legal and political-related factors significantly impact on S2 and S3, while social barriers significantly impact on S1. The effect of the constructs and factors on S1, S2, and S3 along with their rankings are unveiled in this research paper, providing a sound basis to increase success levels and minimize shortfalls in PPP to boost SI developments in developed countries.

A pathway to involve consumers for exchanging electronic waste: a deep learning integration of structural equation modelling and artificial neural network.

The pandemic of COVID-19 has disrupted every human life by putting the global activities at halt. In such a situation, people while staying at home tend to have an increased consumption which also leads to an increased level of waste generation. The case of electronic waste is also not different; however, it has severe repercussions while comparing it with other general household wastes. The application of reverse logistics by the manufacturers though serve the purpose but its success is highly dependent on the participation of the consumers. Hence, the present study is an attempt to gauge the level of participation of the consumers in the reverse exchange programs. Because of the predictability limitations of the typical Structural-Equation-Modelling models, the present study employs the deep learning of the dual-staged partial least squares-structural equation modelling artificial neural network approach. The findings of the study confirms the individual's attitude as the most significant determinant of the intention to exchange, followed by level of awareness and norms, whereas perceived behavior control was found to be least important though significant. Based on these findings, the manufacturers have been recommended to improve the consumers' involvement in reverse exchange programs, whereas government institutions are also recommended to encourage public-private partnerships in channelizing the product returns.