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Adoption of Clinical Decision Support Systems (CDSS) is a crucial step towards the digital transition of the healthcare sector. This review aims to determine and synthesise the influential factors in CDSS adoption in inpatient healthcare settings in order to grasp an understanding of the phenomenon and identify future research gaps.

A systematic literature search of five databases (Medline, EMBASE, PsycINFO, Web of Science and Scopus) was conducted between January 2010 and June 2023. The search strategy was a combination of the following keywords and their synonyms: clinical decision support, hospital or secondary care and influential factors. The quality of studies was evaluated against a 40-point rating scale.

Thirteen papers were systematically reviewed and synthesised and deductively classified into three main constructs of the Technology–Organisation–Environment theory. Scarcity of papers investigating CDSS adoption and its challenges, especially in developing countries, was evident.

This study offers a summative account of challenges in the CDSS procurement process. Strategies to help adopters proactively address the challenges are: (1) Hospital leaders need a clear digital strategy aligned with stakeholders' consensus; (2) Developing modular IT solutions and conducting situational analysis to achieve IT goals; and (3) Government policies, accreditation standards and procurement guidelines play a crucial role in navigating the complex CDSS market.

To the best of the authors’ knowledge, this is the first review to address the adoption and procurement of CDSS. Previous literature only addressed challenges and facilitators within the implementation and post-implementation stages. This study focuses on the firm-level adoption phase of CDSS technology with a theory refining lens.

This chapter advances our understanding of vulnerable consumers by exploring new relationships between resource scarcity and consumer decision-making. Although resource scarcity often prompts individuals to pull back on spending, recent research has shown that it can also increase consumers' motivation to engage in behaviors that fulfill their need for personal control. We extend this stream of research by offering the novel proposition that because resource scarcity motivates the desire for control, activating thoughts about scarcity will increase consumers' interest in products offering self-improvement benefits. We offer initial empirical evidence for when resource scarcity causes consumers to forgo their desire to save by increasing their willingness to pay for products that offer self-improvement benefits. In doing so, this chapter (i) highlights resource scarcity, a state of vulnerability, as an antecedent to the desire for self-improvement, (ii) provides a more nuanced perspective on the motivational underpinnings of resource scarcity and its effects on consumption, and (iii) sheds light on when resource scarcity can increase rather than decrease consumer spending.

Organizational scholars studying compensation often place an emphasis on certain employee groups (e.g., executives). Missing from this discussion is research on the compensation systems for low-wage jobs. In this review, the authors argue that workers in low-wage jobs represent a unique employment group in their understanding of rent allocation in organizations. The authors address the design of compensation strategies in organizations that lead to different outcomes for workers in low-wage jobs versus other workers. Drawing on and integrating human resource management (HRM), inequality, and worker literatures with compensation literature, the authors describe and explain compensation systems for low-wage work. The authors start by examining workers in low-wage work to identify aspects of these workers’ jobs and lives that can influence their health, performance, and other organizationally relevant outcomes. Next, the authors explore the compensation systems common for this type of work, building on the compensation literature, by identifying the low-wage work compensation designs, proposing the likely explanations for why organizations craft these designs, and describing the worker and organizational outcomes of these designs. The authors conclude with suggestions for future research in this growing field and explore how organizations may benefit by rethinking their approach to compensation for low-wage work. In sum, the authors hope that this review will be a foundational work for those interested in investigating organizational compensation issues at the intersection of inequality and worker and organizational outcomes.

Given the remarkable increase in entrepreneurial activities initiated by women in the Egyptian context in addition to the scarcity of empirical studies on digital self-employment there, the authors of the present paper aim to identify what motivates women to engage in digital entrepreneurship, and to identify how those women establish their digital entrepreneurial activities.

The authors employed a qualitative research method through semi-structured interviews with 30 women entrepreneurs who own and manage digital businesses. Thematic analysis was subsequently used to determine the main ideas in the transcripts.

The authors have found that enjoying absolute independence, securing more time for family, guaranteeing an independent source of income in addition to the ease of accessing extensive online markets are the main motives behind the engagement of women in the Egyptian context in digital entrepreneurship activities. Moreover, the authors have also asserted that the minimal training and government support stimulate women entrepreneurs there to start and continue their digital business activities informally.

This paper contributes by filling a gap in entrepreneurship studies in which empirical studies on establishing and managing digital entrepreneurship among women in developing economies has been limited so far.

This study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.

A standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO₂ levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.

Discrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO₂ levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.

This paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.

This study aims to address the question of why managers make different decisions in employee downsizing when their firms face external threats. Our research intends to shed light on whether and how CEOs' cognition (motivational attributes associated with regulatory focus) influences their decision-making and firms’ strategic actions on downsizing under high resource scarcity in the industry environment.

We used a longitudinal panel of 5,544 firm-year observations of US firms from 2003 to 2015 to test our conceptual model. The data was obtained from various sources, including corporate earnings call transcripts and archival databases. We used panel logistic regressions with both fixed and random effects in our research design.

Our results suggest that CEOs' motivational attributes could influence their employee downsizing decisions in response to external threats. We find that CEOs who are more promotion-focused (a stronger drive towards achieving ideals) are less likely to lay off employees during high resource scarcity. Conversely, CEOs with a higher prevention focus (a greater concern for security) do not have a meaningful impact on employee downsizing during periods of external resource scarcity.

Previous research has argued that a significant external threat would diminish individuals' impact on firm strategies and outcomes. Our findings challenge this idea, indicating that CEOs with a stronger drive towards achieving ideals are less inclined to lay off employees when resources are scarce in the environment. This study contributes to behavioral strategy research by providing new insights into how upper echelons’ cognition can influence their decision-making and firms’ employee downsizing.

This paper aims to comprehensively analyze renewable energy alternatives in Brazil, focusing on identifying the most suitable option for investment in the country’s sustainable development.

The study adopts the step-wise weight assessment ratio analysis-multiobjective optimization by ratio analysis −3NAG (a combination of three normalization methods) methodology, a multicriteria decision-making approach, to evaluate and rank renewable energy sources based on key criteria such as resource availability, cost-effectiveness, job creation potential and environmental impact.

The analysis reveals that solar energy emerges as the preferred choice for Brazil, offering significant advantages over other alternatives such as hydroelectric, wind and biomass energy. Solar energy’s distributed generation capability, cost reduction trends and positive environmental impact contribute to its favorable position in meeting Brazil’s energy needs.

While the study provides valuable insights into renewable energy selection, there are limitations regarding the criteria’ scope and the exclusion of specific renewable energy options. Future research could explore sensitivity analyses and incorporate additional criteria to enhance the study’s comprehensiveness.

This research contributes to the existing literature by thoroughly analyzing renewable energy alternatives in Brazil using a robust multicriteria decision-making methodology. The study’s findings provide actionable guidance for policymakers, businesses and stakeholders seeking to promote sustainable energy development in the country.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.