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Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).

This study aims to provide more insight into how customer voice is captured and used in managerial decision-making at the marketing-finance interface. This study’s focus is on understanding how the customer voice, often communicated through online and social media platforms, is used in high-performing hotels.

This research is based on a case study of four high-performing Irish hotels. For each case, multiple informants, including marketing managers, general managers and finance managers, were interviewed and shadowed. Twenty seven decisions across the four cases were analysed to assess the use of customer voice in managerial decision-making.

Social media provides a stage that has empowered the customer voice because of the public nature of the interaction and the network effect. Customer voice is incorporated in managerial decision-making in three distinct ways – symbolically as part of an early warning system, for action-oriented operational decisions and to some extent in the knowledge-enhancing role for tactical decisions. While there is a greater appreciation among senior managers and the finance and accounting managers of the importance of customer voice, this study finds clear limits in its utilisation and more reliance on traditional finance and accounting data, especially in strategic decision-making.

The cases belong to a highly visible open environment of hotels in an industry where customer voice has immediate and strong effects. The findings may not directly apply to industries characterised by a relatively more closed context such as banking or insurance. Moreover, the findings reflect the practices of high-performing hotels and do not necessarily capture the practices used in less successfully operating hotels.

While marketers need to enhance their ability to create a narrative that links the customer voice to revenue generation, finance managers also need to develop a skillset and adopt a mindset that appropriately reflects the influential role for customer voice in managerial decision-making.

Despite the linkage of marketing performance to business performance, there is limited research on the impact of customer information on managerial decision-making. This research provides insight into how customer voice is considered at the critical marketing-finance interface.

This study aims to investigate how two collaborative methods – selection and synthesis – influence knowledge convergence when people articulate a new strategic direction driving transformation within the organization.

The study is based on a longitudinal field experiment developed in four organizations involving 82 employees over a three-month process. Inspired by dynamics governing flocks as complex adaptive systems, selection and synthesis have been separately used in two sets of companies. Primary and secondary data have been largely collected and analyzed throughout the whole process.

This study describes how the two alternative methods differently influenced two kinds of knowledge convergence. While selection triggers a general and static knowledge convergence and the propagation of individual knowledge over time, synthesis fosters a local and dynamic knowledge convergence where individuals tend to propagate knowledge generated collectively.

This research offers insights into understanding the influence of alternative collaborative methods on the creation and propagation of knowledge when people are converging toward a new strategic direction. From a theoretical perspective, it contributes to complex adaptive system theory, highlighting the role of knowledge convergence and emergence through collaboration.

This research offers insights to managers who deal with the complexity of the engagement of different stakeholders during collaborative processes, offering some actionable takeaways to foster knowledge convergence by alternatively employing selection and synthesis.

This paper contributes to the management and social information processing literature emphasizing the role of knowledge convergence emerging from the complex interactions among multiple stakeholders.

This paper aims to examine how competitive intelligence (CI) relates to the strategy formulation process of firms.

Due to the novelty of the phenomenon and to the depth of the investigation required to grasp the mechanisms and logics of CI, a multiple case study has been performed related to four companies located in Brazil that adopted CI practices within dedicated business units to inform and support strategic decision-making.

The authors provide detailed empirical evidence on the connection and use of CI practices throughout each stage of the strategy formulation process. Moreover, the study suggests that CI practices, despite their strategic relevance and diffusion, are still extensively adopted for tactical use.

This study sheds light on how CI practices may inform, support, and be integrated in the strategy formulation process, as few studies have done before.

This study investigated the regulatory landscape surrounding artificial intelligence (AI) in the context of e-government development. The purpose of this article is to identify record-keeping challenges, opportunities and weaknesses that emerge from AI loose regulation. The research focuses on Sweden, Finland and South Africa, examining the interplay between existing guidelines, recommendations and legal structures at various levels.

The research adopted comprehensive systematic and scoping literature reviews, encompassing academic papers, reports and legal documents, along with an analysis of non-academic sources relevant to the study. This methodological approach helped to obtain a deep understanding of the evolving AI regulatory frameworks.

There is currently limited research that focuses on the impact AI deployment has on the management of critical records in government administrations. Also, the findings reveal that AI regulatory environment varies from country to country. The European Union stands as a noteworthy example of a comprehensive framework for AI governance. In contrast, South Africa, while at its infancy stage, demonstrates potential initiatives and policies at different levels. There is emphasis on the need to focus on co-operation, skills development and uniform regulatory frameworks.

This research holds significant practical implications for policymakers, government bodies and stakeholders involved in AI governance. It emphasizes how crucial it is to incorporate AI alongside a solid records management system. The study advocates for strategic investments in education and skills development to enable individuals to navigate the complexities of AI governance.

This research adds to the existing body of knowledge by providing an examination of AI legislation in e-government in the context of public records management. The analysis helps to review literature and other research materials across different geographical areas. The study explores the distinctive strategies used by Sweden, Finland and South Africa. The recommendations offer policymakers and stakeholders suggestions on how to foster effective AI governance and innovation in the public sector but at the same time manage public records effectively.

This paper focuses on knowledge sharing in health care. The aim of the paper is to further understand how digital applications can facilitate knowledge sharing between different care providers and health-care professionals.

The paper is based on a qualitative action case study, performed as a formative intervention study as a Change Laboratory, where a digital application concerning wound support was used. The Change Laboratory was used for knowledge sharing in the assessment and treatment process of wounds. The collected data was then thematically analyzed.

The findings show how digital applications can facilitate knowledge sharing, but also the need for complementary collaborative sessions. The main contribution is the rich description of how digital applications together with these sessions can facilitate knowledge sharing.

This paper shows that activities as collaborative sessions performed on the organizational level prove to support knowledge sharing and learning when a new digital application has been implemented in the work process. It also shows that these sessions contributed to identifying new knowledge that has potential for being included in the application and hence are important to keeping the application updated and relevant over time.

This study aims to examine the challenges in the implementation of energy management systems in residential buildings to lower the running cost and achieve a better energy-efficient building.

This study adopted a mixed research method. Quantitative data was gathered by issuing a research questionnaire to 20 Delphi experts, while qualitative data was acquired through a Systematic Literature Review. Data received was analyzed using the descriptive analysis method.

The findings revealed that the main barriers to incorporating energy management systems (EMSs) in residential buildings consist of a lack of awareness of energy management systems, lack of management commitment to energy management, lack of knowledge about energy management systems, lack of funds for energy management systems, resistance to energy management technology by the property owners and property managers, distrust and resistance to energy management technology by the property owners, high initial cost of energy management technologies, shortage of technicians for energy management technologies, the nonexistence of local manufacturers of energy management equipment, lack of incentives for efficient energy management and high repair costs of energy management technologies.

The specific focus on residential buildings may limit the applicability of findings to commercial or industrial sectors. Further research is warranted to accommodate other energy-consuming sectors.

People’s perceptions, either wrong or correct, affect their ability to make an informed decision to adopt energy management systems, denying them the opportunity to reap the associated benefits. Therefore, there is an urgent need for the residential industry stakeholders and the government to increase educational opportunities for property owners, managers and property tenants on the importance of energy management systems.

This research presents the potential obstacles and problematic areas that residents may encounter while using these energy management systems. Consequently, they will be able to make a well-informed choice when installing energy management systems. Moreover, the research elucidates the identification of novel perspectives and also unexamined obstacles that impede the widespread use of energy management systems in residential buildings.

Halal certification is an acknowledgment of the halalness of a product or service issued by a halal regulator based on Islamic law. This study aims to investigate the intentions of consumers and regulators toward blockchain-based halal certification. Blockchain is useful for storing and verifying halal certificates, thereby increasing trust in products or services because the public cannot change or access data once it is stored.

This study uses a triangulation approach by distributing online questionnaires to consumers as a research instrument of a quantitative approach processed with smart partial least squares. Meanwhile, the qualitative approach is carried out through observation, in-depth interviews with the Ministry of Religion’s Halal Product Assurance Organizing Agency (BPJPH) and Halal Examination Agency (LPH), and forum group discussions (FGDs) with several related parties.

The observation results show that most consumers expect the government to provide an easy-to-use application to check halal food products and restaurants. Consumers’ intention to use this technology is influenced directly by attitudes and indirectly by their beliefs. Furthermore, the results of interviews and FGDs reported that LPH was not ready to apply blockchain technology, while BPJPH strongly supported adopting blockchain technology in the certification process.

This finding recommends that the Indonesian government apply blockchain technology to gain transparency and accountability regarding the halal product process.

This study fills the research gap by observing three perspectives from different stakeholders and using a triangulation approach to analyze the need for adoption of blockchain-based halal certification of halal food products.

This paper focuses on job satisfaction of women employees in the public sector of Afghanistan and identifies factors and challenges influencing women's job satisfaction in their workplace.

The survey data were gathered from 92 employees working at the Ministry of Finance at different position levels.

The findings indicate that women employees highly prioritize salary increment and job promotion as their main job satisfaction factors while mentioning low salary, delay in salary payment and unsuitable workplace as the biggest challenges. Among recommendations, the research suggests to launch workshops for male employees to improve their behavior with women employees in the workplace, to promote employees based on their merit regardless of gender, to standardize salary scales, to develop a chart of female rights highlighting how they should be treated in their workplace and to support the female employees by eliminating gender discrimination and providing a secure work environment free of gender bias.

The findings and recommendations may help public sector organizations as well as the private sector of Afghanistan to improve women employees' job satisfaction.

This article represents an added value for the literature which lacks references about the satisfaction of women working in the public sector of Afghanistan.

Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.

In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario.

It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.

In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.