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This chapter delves into the biopsychosocial reasons behind offending behaviour. It provides an overview of key theories from the biological, psychological and social perspectives in psychology and criminology. The discussion includes different viewpoints on why people offend, focusing on Compassion-Focused Therapy and positive psychology. It explores how these approaches contribute to our understanding of offending behaviour.

We aim to elucidate the relationship between fixed-term employment and firm productivity by examining workers’ skills and considering how firm-level conversion rates influence this relationship.

We use longitudinal employer-employee data between 2011 and 2017 in the Netherlands to estimate a nonlinear regression derived from a production function proposed by Addessi (2014) and Castellani et al. (2020).

The contribution of fixed-term contracts to firm-level productivity is less than that of permanent contracts. However, this contribution is greater when firms exhibit a high conversion rate from fixed-term to permanent positions. The effect of the conversion rate is more substantial for high-skilled fixed-term workers than for low-skilled ones.

Our results suggest the extent to which firms benefit from fixed-term contracts when these are used for screening high-skilled workers for permanent employment.

This article shows how different data sources can be combined to learn about the evolution of gender norms over time. First, data on job advertisements from 1950 up to 2020 reveal that there was a significant change among Swiss employers' stated preferences regarding their prospective employees' gender. More specifically, the proportion of gender-neutral job posts increased from five to almost 95% within the observation period. To further corroborate and contextualize this finding, I complement it with time series on the relative frequency of several specific queries, such as equality between men and women, from Google's German language book corpus. These additional series are broadly consistent with the evolution of the share of gender-neutral job posts. However, it also appears that there are two distinct narratives, one concerned with the personal sphere, identity, and intimate relationships, the other with the political and public realm. Interestingly, the narrative on personal relations set off considerably earlier than the change in the proportion of gender-neutral job ads. Overall, the evidence from the different data series shows that gender norms have changed substantively, yet in a complex manner, over the past decades.

The use of the concept of “agency,” in the sense of action that is to some extent free of “structural” constraints, has enjoyed enormous and growing popularity in the sociological literature over the past several decades. In a previous paper, we examined the range of theoretical rationales offered by sociologists for the inclusion of the notion of “agency” in sociological explanations. Having found these rationales seriously wanting, in this paper we attempt to determine empirically what role “agency” actually plays in the recent sociological literature. We examine a random sample of 147 articles in sociology journals that use the concept of “agency” with the aim of identifying the ways in which the term is used and what function the concept serves in the sociological explanations offered. We identify four principal (often overlapping) uses of “agency”: (1) purely descriptive; (2) as a synonym for “power”; (3) as a way to identify resistance to “structural” pressures; and (4) as a way to describe intelligible human actions. We find that in none of these cases the notion of “agency” adds anything of analytical or explanatory value. These different uses have one thing in common, however: they all tend to use the term “agency” in a strongly normative sense to mark the actions the authors approve of. We conclude that “agency” seems to serve the purpose of registering the authors' moral or political preferences under the guise of a seemingly analytical concept.

The digital content distribution environment is undergoing a dramatic transformation due to the convergence of internet of things (IoT) and over-the-top (OTT) platforms, which provide users with personalised and immersive experiences. OTT streaming platforms have not only grabbed the attention of customers for entertainment and quality content for binge-watch but also successfully changed the industry market trends. An empirical analysis of the deployment of IoT technology in OTT platforms is presented in this chapter. This chapter tries to explore the perception of viewers towards adoption of IoT in OTT streaming platforms. The unified theory of acceptance and use of technology-2 (UTAUT2) model is the main framework for this chapter, and one-way analysis of variance (ANOVA) and stepwise regression is applied to analyse the responses. Findings suggested the consumer characteristics have significant effect on the attitude of the consumers. On the other hand, security and privacy issues with data become major obstacles. In order to balance innovation and user protection, the study concluded with recommendations for OTT service providers and legislators on how to support the responsible and successful implementation of IoT technology in the media and entertainment sector. The findings highlighted that viewers are adopting IoT while streaming OTT platforms. This chapter will help the interested parties and organisations by providing them insights regarding consumer behaviour across OTT services which they can utilise to formulate strategies.

This study aims to investigate the dynamic nature of local economies through the lens of cluster life cycle (CLC) theory. The authors offer an original perspective on the comparison of the mature Old World wine industry and the growing New World one, viewed through the lens of the CLC aiming to understand the specific stage of cluster development for these regions. The authors aim to define the CLC dynamics of two world-renowned wine clusters, symbolizing these old and new world wine industries: Napa and Bordeaux.

The research adopts a case study approach, specifically analyzing the Bordeaux and Napa Valley wine clusters, using a CLC identification framework. This study integrates recent conceptual advances from evolutionary and institutional economic geography, incorporating a dynamic “path” approach to cluster development stages.

In examining Bordeaux, despite indications of maturity in the wine industry, the cluster displays signs of transformation, marked by reorganization, diversification and adaptation to new conditions, such as enotourism and technological clusters. In contrast, Napa Valley, while well established, exhibits characteristics of sustainment with stabilized business dynamics.

This study acknowledges its exploratory nature and the need for future empirical studies on various clusters to fully validate and refine the CLC identification framework.

The proposed CLC identification framework serves as a valuable tool for policymakers and companies by facilitating precise identification of cluster development stages. This tool enables stakeholders to better understand and address the specific needs and characteristics of clusters at different stages, enhancing the effectiveness of targeted interventions and support measures.

This research advances CLC theory by introducing the development trajectories of the Napa and Bordeaux clusters, analyzed independently of their dominant industries, using the CLC path identification model. This approach provides a fresh perspective on cluster evolution, enriching the theoretical framework with insights that transcend traditional industry-specific analysis. By enhancing the precision and relevance of cluster stage identification, this research offers a valuable guide for policymakers and cluster stakeholders, enabling them to better understand the dynamics of cluster development and intervene effectively at critical stages.

This study seeks to improve the understanding of the motivation driving the entrepreneurs of micro, small and medium enterprises (MSMEs) to integrate ICTs and why this process is easier for some. The unified theory of acceptance and use of technology (UTAUT) is a suitable framework for this analysis. Our research aims to establish an explanatory typology based on the optimization of individual perceptions of entrepreneurs and usage intentions which enables identification of those groups that possess a greater intention to use ICTs in their businesses. This sheds light on how these factors influence their information and communication technology (ICT) adoption intentions within their businesses.

This study adopts an alternative approach and methodology to contribute new insights into academic discourse regarding the Unified Theory of Acceptance and Use of Technology (UTAUT). Building upon the theoretical foundation of the UTAUT, the present study pioneers the application of Data Envelopment Analysis (DEA) to a dataset encompassing 436 Spanish entrepreneurs. The objective is to examine the combination of diverse UTAUT perceptions that optimise the intention to implement ICTs in their companies while minimising anxiety associated with the adoption of new technologies (the outputs). Based on the results obtained, an explanatory typology is established that combines perceptions and usage intentions.

When applied to our group of entrepreneurs, DEA reveals that certain individuals can optimise their intentions with their current perception levels. This discovery has enabled us to create an explanatory typology through an examination of the efficiency of various perception-intention and/or anxiety combinations. Our proposed typology aims to shed light on the debate as to why not all perceptions translate into intentions and why certain entrepreneurs have a greater intention to use ICTs. An investigation of these efficient entrepreneurs reveals a wide range of combinations, which encompasses both those in harmony with effective usage behaviour and those deviating from such behaviour.

DEA provides a snapshot of efficiency at a specific point in time and does not account for dynamic changes or adjustments over time, and DEA efficiency scores are relative measures that depend on the efficiency of other decision-making units in the dataset. The identification of appropriate benchmark units for comparison can be challenging, especially in heterogeneous datasets and cross-cultural analysis. In this respect, the UTAUT literature is lacking in cross-technology and cross-cultural comparisons.

Our methodology and results have implications for business management, business accelerators and economic policy. A detailed study of these clusters could reveal potential barriers and obstacles hindering the effective implementation of ICTs in MSMEs, thereby enabling researchers to focus on individuals who do not align with the model. Entrepreneurs classified in the most unfavourable typologies can take steps to enhance their perceptions, while administration and business accelerators can focus their efforts on these entrepreneurs.

The application of the UTAUT model on individual entrepreneurs receives limited coverage in the existing literature. To the best of our knowledge, this is the first study to utilise a DEA methodology within the framework of the UTAUT model (in contrast to the prevalent use of structural equation modelling in previous studies related to UTAUT). This analysis contributes fresh empirical evidence to the academic discourse on technology adoption models among individual entrepreneurs. Our methodology is a tool that reveals potential barriers and obstacles in individual perceptions that hinder the effective implementation of ICT in certain types of entrepreneurs.

Users contribute to online communities by posting and responding to discussion threads. Nonetheless, only a small fraction of threads gain popularity and shape community discourse. Prior studies have identified several factors driving thread popularity; however, despite their prevalence, the role of emotional expressions within discussion threads remains understudied. This study addresses this gap by investigating the impact of thread starters’ valence and embedded discrete emotions of anger, anxiety and sadness on thread popularity, drawing on the negativity bias and the emotion-as-social-information theories.

Using two samples from Reddit, this study employs negative binomial regression analysis to examine the hypothesized relationships.

The results demonstrate that negativity in thread starters significantly influences thread popularity; however, the expression of discrete emotions impacts popularity variously. In some contexts, such as COVID-19 vaccination subreddits, embedded anger in thread starters decreases thread popularity, whereas anxiety and sad expressions enhance it. In other contexts, such as professional discussions (e.g. r/Medicine subreddit), anger and anxiety expressions increase thread popularity, while sad expressions have no significant influence.

The study is limited by its focus on specific emotions and contexts. Future research could examine a broader range of emotions, post-content modalities and the impact of cultural and linguistic differences.

This study contributes to theory by offering a new definition of thread popularity and enhancing our understanding of the impact of emotions in online discussions. It also provides practical implications for online community members and moderators seeking to promote discussion posts that help achieve community goals.

The construction and real estate sectors are vital to national economies, but traditional construction methods often lead to challenges such as safety risks, noise and environmental pollution. While intelligent construction is believed to mitigate these issues, there is a lack of solid empirical evidence on whether it truly benefits the general public. This paper seeks to explore the societal benefits of intelligent construction from the public’s perspective, addressing this research gap.

The research adopts a two-step approach. First, topic mining is conducted to identify topics closely related to the public’s daily life, such as environmental impact, construction traffic management and construction technologies. These topics are then analyzed through sentiment analysis using a bidirectional long short-term memory model with attention mechanism to determine whether the public has a favorable view of these aspects of intelligent construction, indirectly demonstrating the benefits to the public.

The primary topics identified include “industry development,” “technology enterprise,” “construction equipment,” “intelligent technology,” “environmental protection,” “robots” and “construction traffic management.” Sentiment analysis shows that public sentiment is overwhelmingly positive across all topics and regions, with “environmental protection,” “construction traffic management” and “robots” receiving the most favorable reactions.

This study provides empirical evidence of the societal benefits of intelligent construction from the public’s viewpoint using social media data. The results highlight the need for continued promotion and adoption of intelligent construction due to its positive impact on society.