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This paper aims to verify whether the integration of sustainability in executive compensation positively affects firms’ non-financial performance and whether corporate governance characteristics enhance the relationship between sustainability compensation and firms’ non-financial performance and to expand the domain of the impact of sustainability on non-financial performance.

This analysis is based on a sample of companies listed on the Milan Italian Stock Exchange from the Financial Times Milan Stock Exchange Index over the 2016–2020 period. Regression analysis was used by using data retrieved from the Refinitiv Eikon database and the sample firms’ remuneration reports.

The findings of this paper show that embedding sustainability in executive compensation positively affects firms’ non-financial performance. The results of this paper also reveal that specific corporate governance features can improve the impact of sustainability on non-financial performance.

This analysis is limited to Italian firms included in the Financial Times Milan Stock Exchange Index; however, the findings are highly significant.

The findings provide regulators with useful insights for considering the integration of sustainability goals into executive remuneration. Another implication is that policymakers should require – at least – listed firms to fulfil specific corporate governance structural requirements. Finally, the findings can provide investors and financial analysts with a greater awareness of the role played by executive remuneration in the long-term value-creation process.

This paper contributes to addressing the relationship among sustainability, remuneration and non-financial disclosure, drawing on the stakeholder–agency theoretical framework and focusing on Italian firms. This issue has received limited attention with controversial results in the literature.

During the evaluation of European Capital of Culture (ECoC) Aarhus 2017, the evaluation organisation rethinkIMPACTS 2017 formulated a set of “dilemmas” capturing the main challenges arising during the design of the ECoC evaluation. This functioned as a framework for the evaluation process. This paper aims to present and discuss the relevance of the “Evaluation Dilemmas Model” as subsequently applied to the Galway 2020 ECoC programme evaluation.

The paper takes an empirical approach including auto-ethnography and interview data to document and map the dilemmas involved in undertaking an evaluation in two different European cities. Evolved via a process of practice-based research, the article addresses the development of and the arguments for the dilemmas model and considers its potential for wider applicability in the evaluation of large-scale cultural projects.

The authors conclude that the “Evaluation Dilemmas Model” is a valuable heuristic for considering the endogenous and exogenous issues in cultural evaluation.

The model developed is useful for a wide range of cultural evaluation processes including – but not limited to – European Capitals of Culture.

What has not been addressed in the academic literature is the process of evaluating ECoCs; especially how evaluators often take part in an overall process that is not just about the evaluation but also planning and delivering a project that includes stakeholder management and the development of evaluation criteria, design and methods.

The purpose of this research is to explore how an industry is represented in multimodal public media narratives and to explore how this representation subsequently affects the formation of public sense-giving space during a persisting crisis, such as a pandemic. The question asked is: how do the use of multimodality by public service media dynamically shape representations of industry identity during a persisting crisis?

This study made use of a multimodal approach. The verbal and visual media text on the restaurant industry during the COVID-19 pandemic that were published in Finland by the public service media distributor Yle were studied. Data published between March 2020 and March 2022 were analysed. The data consisted of 236 verbal texts, including 263 visuals.

Three narratives were identified– victim, servant and survivor – that construct power relations and depict the identity of the restaurant industry differently. It was argued that multimodal media narratives hold three meaning making functions: sentimentalizing, juxtaposing and nuancing industry characteristics. It was also argued that multimodal public service media narratives have wider implications in possibly shaping the future attractiveness of the industry and organizational members' understanding of their identity.

This research contributes to sensemaking literature in that it explores the role of power – explicitly or implicitly constructed through media narratives during crisis. Furthermore, this research contributes to sensemaking literature in that it shows how narratives take shape multimodally during a continuous crisis, and how this impacts the construction of industry identity.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.

This study aims to investigate the relationship between corporate governance (CG) and financial performance in the case of publicly listed companies in Vietnam for the period from 2019 to 2021. The topic is crucial in understanding how effective governance practices can influence the financial outcomes of companies. The study sheds light on the link between CG practice and firm financial performance. It also provides insights for policymakers and practitioners to improve CG practices.

Due to the potential dynamic endogeneity in CG research, this study uses the generalized system methods of moments to effectively address the endogeneity problem. Financial performance is measured by Tobin’s Q, return on equity (ROE) and return on assets (ROA). Based on organization for economic cooperation and development (OECD) standards, these indices were calculated to assess the influence of CG practices on corporate financial performance, namely, for accounting information (ROA and ROE) and market performance (Tobin’s Q and service à resglement différé (SRD) – stock price volatility) for the period 2019–2021. In addition, the study examines the relationship between changes in the CG index and changes in financial performance.

The study’s main objective is to determine the relationship between CG performance scores and financial performance. The study found a positive relationship between transparency disclosure and financial performance and a positive correlation between CG and company size. The COVID-19 pandemic caused a decrease in transparency and information index scores in 2021 compared to 2019 and 2020 due to delayed General Meetings of Shareholders. The study failed to find a relationship between shareholder rights index (“cg_rosh”) and board responsibility (“cg_reob”) and financial performance, concerning which the findings of this study differ from those of previous studies. Reasons are put forward for these anomalies.

Policymakers need to develop a set of criteria for assessing CG practices. They also need to promulgate specific regulations for mandatory and voluntary information disclosure and designate a competent authority to certify the transparency of company information. The study also suggests that companies should develop CG regulations and focus on regulations relating to the business culture or ethics, as well as implementing a system to ensure equal treatment among shareholders. The study found that good CG practices can positively contribute to a company’s financial performance, which is crucial for investors to evaluate the quality of CG practices for each listed company so that investment risks can be limited.

The purpose of this paper is to explore the concept of data assets and how companies can assetize their data. Using the literature review methodology, the paper first summarizes the conceptual controversies over data assets in the existing literature. Subsequently, the paper defines the concept of data assets. Finally, keywords from the existing research literature are presented visually and a foundational framework for achieving data assetization is proposed.

This paper uses a systematic literature review approach to discuss the conceptual evolution and strategic imperatives of data assets. To establish a robust research methodology, this paper takes into account two main aspects. First, it conducts a comprehensive review of the existing literature on digital technology and data assets, which enables the derivation of an evolutionary path of data assets and the development of a clear and concise definition of the concept. Second, the paper uses Citespace, a widely used software for literature review, to examine the research framework of enterprise data assetization.

The paper offers pivotal insights into the realm of data assets. It highlights the changing perceptions of data assets with digital progression and addresses debates on data asset categorization, value attributes and ownership. The study introduces a definitive concept of data assets as electronically recorded data resources with real or potential value under legal parameters. Moreover, it delineates strategic imperatives for harnessing data assets, presenting a practical framework that charts the stages of “resource readiness, capacity building, and data application”, guiding businesses in optimizing their data throughout its lifecycle.

This paper comprehensively explores the issue of data assets, clarifying controversial concepts and categorizations and bridging gaps in the existing literature. The paper introduces a clear conceptualization of data assets, bridging the gap between academia and practice. In addition, the study proposes a strategic framework for data assetization. This study not only helps to promote a unified understanding among academics and professionals but also helps businesses to understand the process of data assetization.

The efficiency of each of an organization’s individual workers determines its effectiveness. The study aims to explore the relationship between human resource management (HRM) practices and organizational effectiveness with employee performance as a mediating variable.

Data were collected from 800 police officers in the Greater Accra and Tema regions. The data were supported by the hypothesized relationship. Construct reliability and validity was established through confirmatory factor analysis. The proposed model and hypotheses were evaluated using structural equation modeling.

The results show that career planning and employee performance were significantly related. Self-managed teams and employee performance were shown to be nonsignificantly related. Similarly, performance management and employee performance were shown to be nonsignificantly related. Employee performance significantly influenced organizational effectiveness. The results further indicate that employee performance mediates the relationship between HRM practices and organizational effectiveness.

The generalizability of the findings will be constrained due to the research’s police service focus and cross-sectional data.

The study’s findings will serve as valuable pointers for the police administration in the adoption, design and implementation of well-articulated and proactive HRM practices to improve the abilities, skills, knowledge and motivation of officer’s to inordinately enhance the effectiveness of the service.

By evidencing empirically that employee performance mediates the relationship between HRM practice and organizational effectiveness, the study extends the literature.