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The objective of this paper is to develop a redesigned commissioning process for social care services that fosters integrated care, encourages collaboration and balances professional expertise with client engagement.

This study employs a two-pronged approach: a case study of a municipality’s use of subsidy tables and a literature scoping review on integrated care research.

The paper introduces a new framework for the study of the new “subsidy tables.” A well-defined and extensive consultation process involving both social care providers (suppliers), the Service Triad, and client representation adds to the existing research on supplier consultation, and on how to define the outcomes for clients via client engagement.

While aspects are clearly relevant to the Netherlands, the design of the commissioning process of social care has international relevance as well: finding definitions, formulating outcomes and incentives, designing a more collaborative instead of competitive process, stakeholder engagement and consultation.

Several Dutch municipalities started using the “subsidy tables” method for commissioning integrated social care. This paper offers clear improvements that benefit the commissioners, the social care providers and their clients.

Improving the commissioning process of integrated social care will lead to better fitting care for people who need social care.

This paper is one of the first to do a thorough analysis of the “subsidy tables” method for commissioning integrated social care.

Compared to traditional (domestic) e-commerce consumers, cross-border electronic commerce (CBEC) consumers may face greater information asymmetry in the CBEC purchase process. Given this background, however, the literature has paid limited attention to the informational antecedents that influence consumers' perceptions of transaction costs and their CBEC purchase intentions. To fill this gap, this study integrates the elaboration likelihood model (ELM) and transaction cost theory (TCT) to develop a model for exploring how product (website informativeness, product diagnosticity and website interactivity as the central route) and external (country brand, website policy and vendor reputation as the peripheral route) informational antecedents affect consumers’ evaluations of transaction costs in terms of uncertainty and asset specificity and their CBEC purchase intentions.

This study employs a survey approach to validate the model with 766 Generation Z CBEC consumers based on judgment sampling. The partial least squares (PLS) technique is adopted for data analysis.

The results show that all the proposed central and peripheral informational antecedents reduce consumers’ perceptions of uncertainty and asset specificity, which in turn negatively influences their CBEC purchase intentions.

Through this investigation, this study increases our understanding of how product and external informational antecedents affect consumers’ evaluations of transaction costs, which subsequently determine their CBEC purchase decisions. This study offers theoretical contributions to existing CBEC research and has practical implications for CBEC organizations and managers.

Consumers’ digital engagement can bring various benefits to both brands and consumers. Besides, few studies investigated the outcomes of engagement with restaurant brands on Instagram. Therefore, this study aims to examine the effect of consumer engagement (CE) with restaurant brands on consumer-related factors (namely, consumer’s brand knowledge, perceived enjoyment and consumer social interaction) and brand-related factors (namely, e-WOM and brand reputation), as well as the mediating role of consumer-related factors.

The sample consisted of 394 Instagram followers of restaurant/coffee shop brands, and covariance-based structural equation modeling and bootstrapping were used to assess the hypothesized relationships.

The results show that CE with restaurant brands on Instagram enhances brand-related outcomes as well as consumer-related outcomes. Moreover, consumer-related factors partially mediate these relationships.

The findings of this study provide insights for restaurant managers and digital marketers to stimulate consumer-brand engagement.

To the best of the authors’ knowledge, this study is among the first that examines the effect of CE with restaurant brands on consumer- and brand-related outcomes on Instagram. The context of the study is Iran, which adds to the literature on CE that mainly focuses on developed countries.

This study aims to find the key drivers of green innovation in family firms by examining firm characteristics and geographical factors. It seeks to develop a conceptual framework that explains how internal resources and external environments influence environmental innovation practices in these businesses.

Using machine learning (ML) methods, this study develops a predictive model for green innovation in family firms, drawing on data from 3,289 family businesses across 27 EU Member States and 12 additional countries. The study integrates the Resource-Based View (RBV) and Location Theory to analyze the impact of firm-level resources and geographical contexts on green innovation outcomes.

The results show that both firm-specific resources, such as size, digital capabilities, years of operation and geographical factors, like country location, significantly influence the likelihood of family firms engaging in environmental innovation. Larger, technologically advanced firms are more likely to adopt sustainable practices, and geographic location is crucial due to different regulatory environments and market conditions.

The findings reinforce the RBV by showing the importance of firm-specific resources in driving green innovation and extend Location Theory by emphasizing the role of geographic factors. The study enriches the theoretical understanding of family businesses by showing how noneconomic goals, such as socioemotional wealth and legacy preservation, influence environmental innovation strategies.

Family firms can leverage these findings to enhance their green innovation efforts by investing in technology, fostering sustainability and recognizing the impact of geographic factors. Aligning innovation strategies with both economic and noneconomic goals can help family businesses improve market positioning, comply with regulations and maintain a strong family legacy.

This research contributes a new perspective by integrating the RBV and Location Theory to explore green innovation in family firms, highlighting the interplay between internal resources and external environments. It also shows the effectiveness of machine learning methods in predicting environmental innovation, providing deeper insights than traditional statistical techniques.