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The purpose of this paper is to analyse the possibility for firms to consider institutional settings to systematically direct dispersed individual efforts of discovery and invention towards objects (products or processes) of their interest in order to enhance their value creation capacity.

The authors conduct a comparative analysis of the different institutional settings within which software products are invented and produced – closed producer-centred model, open user-centred model, and hybrid interactive producer-user model.

The authors draw indications regarding the possibility to design institutional settings for value creation and the potential pitfalls tied to these strategic tools.

A theoretical framework is elaborated in order to understand the different ways in which institutional contexts influence and direct value creation processes. The model analysed shows the firms’ deliberate attempt to stimulate a dynamic process of social interaction and communication which may foster higher levels of creativity and innovation. In order to guarantee the necessary accessibility and to sufficiently motivate external programmers towards the perception of a new code, the firm has to surrender the traditional source through which it appropriates value: barriers to the accessibility of the code developed through IPRs. The adoption of an institutional setting which facilitates dynamic value creation processes suggests, therefore, the need to turn to dynamic mechanisms for value appropriation in parallel.

Process redesign refers to the intentional change of business processes. While process redesign methods provide structure to redesign projects, they provide limited support during the actual creation of to-be processes. More specifically, existing approaches hardly develop an ontological perspective on what can be changed from a process design point of view, and they provide limited procedural guidance on how to derive possible process design alternatives. This paper aims to provide structured guidance during the to-be process creation.

Using design space exploration as a theoretical lens, the authors develop a conceptual model of the design space for business processes, which facilitates the systematic exploration of design alternatives along different dimensions. The authors utilized an established method for taxonomy development for constructing the conceptual model. First, the authors derived design dimensions for business processes and underlying characteristics through a literature review. Second, the authors conducted semi-structured interviews with professional process experts. Third, the authors evaluated their artifact through three real-world applications.

The authors identified 19 business process design dimensions that are grouped into different layers and specified by underlying characteristics. Guiding questions and illustrative real-world examples help to deploy these design dimensions in practice. Taken together, the design dimensions form the “Business Process Design Space” (BPD-Space).

Practitioners can use the BPD-Space to explore, question and rethink business processes in various respects.

The BPD-Space complements existing approaches by explicating process design dimensions. It abstracts from specific process flows and representations of processes and supports an unconstrained exploration of various alternative process designs.

Disruptive technologies for human resource management (HRM) lack evidence about synthesizing its antecedents and outcomes in the empirical research landscape. This review study was conducted addressing two objectives: (1) to identify the themes of HRM technology empirically researched and (2) to develop a conceptual framework for the antecedents and outcomes of disruptive technologies for HRM.

This article was positioned as a literature review based on the systematic literature review (SLR) methodology. Seventeen articles published between 2000 and 2020 were included for the review from Scopus and LENS.ORG databases. The article screening, inclusion and analysis and findings reporting were based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA).

In addressing the first objective, the review found that the electronic human resource management (E-HRM) intention and communication technology support impact E-HRM adoption, resulting in many outcomes. The outcomes include HRM service quality, organizational effectiveness, employee productivity and competitive advantage. In addressing the study's second objective, a conceptual framework was developed based on the gaps identified regarding the determinants and positive outcomes of disruptive technologies for HRM. Thus, the conceptual framework was presented from two perspectives. The perspectives were deduced with review results and theoretical grounds.

The findings related to the two objectives provide theoretical and practical implications. One significant practical implication is that action and work-based learning of managers and employees in organizations may suggest adopting disruptive technologies for HRM. Thus future studies can focus on them as determinants of adopting disruptive technologies for HRM to conduct empirical tests. Besides, the findings provide some other future research directions.

The study found empirically proven determinants of E-HRM intention and communication technology support impact E-HRM adoption, which results in many positive outcomes. These were synthesized based on the studies conducted during the period 2000–2020. This synthesis and theoretical grounding deduced a conceptual framework for determinants and outcomes of disruptive technologies for HRM.

The purpose of this study is to quantify port efficiency assessment indicators to analyze the impact of COVID-19 on Chinese One Belt One Road (OBOR) ports.

This study utilized a grey prediction model GM(1,1) to forecast five relevant indicators for each of the 17 OBOR ports both with and without COVID-19 background conditions. Additionally, the data envelopment analysis (DEA) efficiency assessment approach was used to analyze the impact of COVID-19 on port efficiency.

The results indicate that cargo and container throughput growth rates during the COVID-19 pandemic are reduced by 1.7 and 2.1%, respectively. There was also a noticeable reduction in technological efficiency (TE) as well as pure technological efficiency (PTE), while scale efficiency (SE) remained largely unaffected. Furthermore, the dynamic efficiency MI was mainly negatively impacted by changes in overall efficiency change (EFFCH), where pure efficiency change (PECH) less than one contributed significantly towards overall regression of port efficiencies during this period.

This paper is unique in its use of a combination of the grey prediction model and DEA efficiency assessment to quantify changes in important indicators during pandemic periods. This approach not only provides a quantitative understanding of the impact on port-level efficiency through numerical quantification but also offers readers an intuitive understanding.

The purpose of this paper is to develop an original model and a solution procedure for solving jointly three main strategic fleet management problems (fleet composition, replacement and make-or-buy), taking into account interdependencies between them.

The three main strategic fleet management problems were analyzed in detail to identify interdependencies between them, mathematically modeled in terms of integer nonlinear programing (INLP) and solved using evolutionary based method of a solver compatible with a spreadsheet.

There are no optimization methods combining the analyzed problems, but it is possible to mathematically model them jointly and solve together using a solver compatible with a spreadsheet obtaining a solution/fleet management strategy answering the questions: Keep currently exploited vehicles in a fleet or remove them? If keep, how often to replace them? If remove then when? How many perspective/new vehicles, of what types, brand new or used ones and when should be put into a fleet? The relatively large scale instance of problem (50 vehicles) was solved based on a real-life data. The obtained results occurred to be better/cheaper by 10% than the two reference solutions – random and do-nothing ones.

The methodology of developing optimal fleet management strategy by solving jointly three main strategic fleet management problems is proposed allowing for the reduction of the fleet exploitation costs by adjusting fleet size, types of exploited vehicles and their exploitation periods.

The study focuses on academic stress among the students due to insufficient efforts and intrapersonal conflicts amid the COVID-19 situation. The purpose of this study is to measure the mediating role of academic stress between life dissatisfaction and adequate steps and intrapersonal conflicts.

The researcher had surveyed 729 students from the two states, including Haryana and Punjab and 716 responses were used for analysis. These respondents were approached in April–May 2020 to explore the impact of COVID-19. The questionnaire was prepared with the help of Google form, including 29 questions. Confirmatory factor analysis and structural equation modeling (SEM) were used to validate the research model and for testing the hypothesis.

The findings indicate that intra-personal conflict and insufficient efforts have a significant positive relation with academic stress. Also, intrapersonal conflict and insufficient efforts have a significant positive relation with life dissatisfaction. It has been reported that academic stress mediates the relationship between life dissatisfaction and intra-personal conflict. The results also reveal that academic stress is positively and significantly associated with life dissatisfaction.

This is one of the few studies that explored the mediating role of academic stress between life dissatisfaction and insufficient efforts and intrapersonal conflicts.

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.

Despite increasingly persuasive women-focused marketing of gambling products, there has only been limited investigation around women sports betting. Men remain the focus of much of the conversation about sports betting as they have generally been found to be the most active sports bettors and the most at risk of experiencing harms associated with their behaviour. This chapter aims to fill this gap by exploring the characteristics of young women sports bettors in the United Kingdom and the relationship between sports betting and the experience of gambling harms. To do this, we created two models of analysis. Our analysis is based on data from the first wave (2019) of the Emerging Adults Gambling Survey (EAGS) dataset (n = 3,549). The EAGS is a non-probability longitudinal survey that includes individuals between the ages of 16 and 24 who were residents in Britain at the time of data collection. Firstly, we examined the associations between women sports bettors and several factors identified as important predictors of sports betting. Secondly, we sought to understand the relationship between women's sports betting and the harms associated with this activity. From these models, we found that women's sports betting was most reliably predicted according to fandom and peer influence. We also found that women sports bettors were more at risk of experiencing harms associated with difficulties with family and friends than women gamblers using other products.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

Targets set out by state institutions, with respect to supplying water to deprived communities, seem to be idealistic and not realistic. Study envisioned to assess challenges of financing water infrastructure projects, and determines the role of the state towards infrastructure development by holistically planning and engaging with the private sector.

The study adopted a quantitative approach, whereby a questionnaire survey was conducted among different stakeholders involved in water infrastructure projects in South Africa. Data gathered were analysed using percentages, mean item score and standard deviation.

The study revealed that most challenges affecting the success of the financing of water infrastructure projects in South Africa are corruption, hostility towards private participation, cost recovery constraints, high fiscal deficits by state government, unreliable planning and procurement processes, and a rapid increasing number of municipalities that lack technical and administrative capacity to plan implement, operate and maintain water assets.

This research paper investigates projects’ financing challenges with a broad inspection on the role of the public sector. The apparent role of the international structures such as OECD, IMF and World Bank had no influence in the study. From the findings, it is clear that the central government and state institutions lack the necessary resources to accelerate infrastructure development, water infrastructure in particular. The study, thus, recommends a complete expansion and development of state capacity as well as improved collaborations with the private sector to drive the success delivery of services to the public.

Improved and flexible regulations and legislative guidelines are required to ensure that both sectors fulfil their side of the bargain, with an ultimate goal of meeting the predetermined targets of supplying adequate water to the deprived communities.