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Ah la famille …! We tend to say that we do not choose it. But there are beautiful family stories, even in business, and particularly in France. Indeed, when it comes to business, the French take family as a serious matter – with about 80% of all companies in the country family controlled. Whether big or small, French family businesses are particularly noticeable in sectors such as food and beverages, as well as luxury.

The chapter gives a general overview of family firms in France, considering in particular their main legal structures, how diverse they are in reality, and finally their governance rules, and notably their family constitutions. It concludes that business and family stories often prove to be a good match, at least in France.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.

Since the end of the twentieth century a movement has been observed, both in geography of tourism and in fashion, spreading to districts located on the outskirts of cities. This is the case of Château Rouge, located in the 18th arrondissement of Paris. For years considered as a “no-go-zone,” the district is now experiencing parallel phenomena of “off-the-beaten-tracks” tourism and fashion development. This chapter analyzes the processes and actors behind the expansion of the tourism perimeter and the diversification of fashion venues, as well as the implications of these developments for the city of Paris.

The purpose of this empirical research was to investigate the individual effects of three dimensions of flexible human resource management (HRM) practices – skill flexibility, behavioral flexibility and human practice flexibility on firm performance and to what extent these relationships are strengthened/weakened when there was the moderation of empowering leadership in small- and medium-sized enterprises (SMEs) of the Kingdom of Bahrain.

The data were collected from chief executive officers (CEOs) through a questionnaire survey method to test the hypothesized relationships. A final dataset of 315 valid responses was utilized for data analysis, and results were analyzed using the Smart partial least squares structural equation modeling (PLS-SEM) technique.

The findings revealed positive effects of skill flexibility, behavioral flexibility and human practice flexibility on firm performance. The moderating role of empowering leadership further strengthened the effects of employee skill flexibility and employee behavioral flexibility had on firm performance. However, empowering leadership did not pose any moderating effect on human practice flexibility and firm performance relationship.

This paper offers implications for theories on HRM and leadership. It also provides valuable insights for organizations and leaders seeking to boost firm performance across SMEs.

This paper offers implications for theories on HRM and leadership and also contributes in the understanding of the modern managers.

This paper investigated the effects of flexible HRM practices on firm performance and the role of empowering leadership across SMEs in Bahrain. It also explored how the performance of SMEs can be improved using flexible HRM practices followed by the presence of empowering leadership.

Emphasizing the increasing role of talent management (TM) as a global phenomenon and a source of sustainable competitive advantage for organizations, this study aims to present a scoping review of empirical literature on TM, examining the transition of TM from a phenomenon-driven to a theory-driven field.

Using a scoping review, this study analyzed 200 empirical studies published between 2010 and 2023 on TM.

The results indicate that TM is extensively studied in nationally operated, large, private, engineering-led organizations in Anglo-Saxon countries. The study highlights the necessity for more empirical studies and statistically robust evidence to establish the effectiveness of TM.

This review intends to provide a vision and direction for future researchers, guiding TM towards becoming a theory-driven field characterized by widely accepted theoretical frameworks and research designs.

The findings of this study may not be generalizable to other types of organizations or cultural contexts, as it primarily focused on large private engineering-led organizations in Anglo-Saxon countries.

This paper offers a comprehensive view of the definitions, contextualization, conceptualization, frameworks, practices, processes and under-explored areas of TM, which are essential for its development as a discipline.

The aim of this paper is to better understand European consumers' behaviour in relation to Short Food Supply Chains (SFSCs), so as to provide insights to support their development as part of a sustainable food system. Specifically, it aims to analyse consumer purchase patterns, motivations and perceived barriers and to identify patterns of behaviour amongst different consumer groups.

An online consumer survey was conducted in 12 European countries (n = 2,419). Quantitative data analysis, including principal component analysis (PCA) and cluster analysis, was undertaken using SPSS.

Four consumer clusters are named according to their behavioural stage in terms of SFSC engagement: Unaware Unengaged, Aware Unengaged, Motivationally Engaged and Executively Engaged. Unaware Unengaged and Aware Unengaged are in the non-engagement phase of behaviour. Motivationally Engaged are motivationally activated to engage in the behaviour but fail to do so consistently. Executively Engaged is the fully engaged cluster, being motivated to act and purchasing local food on a frequent basis. The results show an interesting interplay between motivations and barriers, i.e. higher scores for motivations and lower scores for barriers do not necessarily translate into higher purchase frequency.

The research gleans insights into the contextual factors that may inhibit SFSC purchases in different consumer segments. It offers practical implications for policymakers and others seeking to develop SFSCs as part of a sustainable food system.

This aim of this study is to identify the ways of helping public authorities bring about change to environmentally sustainable household food practices.

The authors identified the practices involved in this concept from the consumer perspective and measured their diffusion among French households. The analyses were conducted following two successive data collection campaigns comprising 571 and 501 respondents in France. The methodology involved two complementary scaling techniques: factor analysis and item response theory.

The results show that consumers understand sustainable food through five food practices: buying and cooking products with sustainable attributes, anti-waste storage, self-production, plant protein consumption and anti-waste cooking.

The findings suggest that while at the individual level people appear to have incorporated anti-waste practices into their daily lives, at the household level, there is still work to be done for improving diets and stimulating the production of home-grown food. It is also worth noting that the emerging vision typically involves sustainable foods that are organic, locally grown, seasonal, based on fair trade and packaging-free.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

Reverse logistics (RL) has become integral in modern supply chains, with many companies investing in circular economy (CE), a recuperative and effective industrial economy. The traditional linear model triggered many negative environmental consequences such as climate change, ocean pollution, loss of biodiversity and land degradation. The development of RL strategies that support the transition between RL to CE is crucial. The purpose of this paper is to connect RL with CE in the context of Industry 4.0 and develop a hierarchal structure to explore the relationship between RL and CE critical success factors in the context of Industry 4.0.

This study used both qualitative and quantitative approach. Literature review in collaboration with the Delphi method is used to identify and validate critical success factors. Then, the ISM-based model and MICMAC method were used to determine the relationship between CE and RL success factors and its driving and dependence power.

This study result shows that waste reduction, skilled employees and expert's involvement and top management commitment and support will provide guidelines and paths for implementing CE and RL, leading to the competitiveness of a firm.

The findings provide managerial insight, particularly useful to third-party logistics companies' managers who are looking to implement RL and CE, to help prioritize where to invest company resources to generate prime difference. Furthermore, this study also identified Industry 4.0 technologies, which would tackle top identified critical success factors within the hierarchical model such as block chain and digital platforms.

This paper contributes to the literature by exploring the connection between RL and CE in the context of Industry 4.0 that determines the critical success factors enabling sustainable inter-firm collaboration.