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The purpose of the article was to identify the core dimensions of strategic thinking and create a measure that provides a comprehensive operationalization of the construct.

The construct validity of the measure was assessed in two studies using four samples with a total of 985 participants. The measure was created using a multi-step process that included item development and content validation, exploratory and confirmatory factor analysis, convergent and discriminant validity, criterion validity and test-retest validity.

The exploratory factor analysis (EFA) supported the existence of the three dimensions of strategic thinking (visionary, synthetic and creative thinking) as conceptually proposed. The measure was reduced to nine items. The confirmatory factor analysis (CFA) confirmed the three dimensions and revealed acceptable factor loadings and model fit. Convergent, discriminant and criterion validity were established, and the measure demonstrated acceptable test-retest reliability.

An individual's ability to think strategically is vital for making strategic decisions and relevant to upper echelon theory and strategic management. The definition and core dimensions of strategic thinking are unclear in the literature, creating confusion. This study added to the literature by defining the core dimensions of strategic thinking and developing the strategic thinking assessment (STA) to measure the construct.

This article describes the development processes, sampling and analysis practices and the assessment of reliability and validity of a new 0survey that sought to evaluate undergraduate students' perceptions and expectations related to privacy and library participation in learning analytics studies. This article provides other researchers with information required to independently evaluate the survey's efficacy, as well as guidance for designing other surveys.

Following question development, pre-survey validity assessments were made using subject matter expert panel review and cognitive interviews. Post-hoc analysis of survey construct reliability was evaluated using the Omega coefficient, while exploratory factor analysis was utilized to assess construct validity. Survey design limitations and potential bias effects are also examined.

The survey exhibited a high level of reliability among research constructs, while the exploratory factor analysis results suggested that survey constructs contained multiple conceptual elements that should be measured separately for more nuanced analysis.

This article provides a model for other researchers wishing to re-use the survey described or develop similar surveys.

As learning analytics interest continues to expand, engaging with the subjects, in this case students, of analysis is critical. Researchers need to ensure that captured measurements are appropriately valid in order to accurately represent the findings.

This survey is one of very few addressing library learning analytics that has undergone extensive validity analysis of the conceptual constructs.

Consumers are assumed to engage in external information search only after exhausting their internal information sources. Guided by the accessibility/diagnosticity and ease-of-retrieval frameworks, and the elaboration likelihood model, the current study investigates this phenomenon.

To test the relationships between internal information accessibility/diagnosticity and the importance of external search, and the moderating role of involvement in these relationships, 308 responses were collected on Amazon MTurk. Then, structural equation modeling was employed to analyze the data.

The analyses showed that while accessibility and diagnosticity of internal information have an impact on external information search, involvement with the product class has a consequential moderating effect on these relationships. In particular, in the low-involvement group, only the diagnosticity of internal information had a negative effect on external information search. On the contrary, in the high-involvement group, only accessibility of internal information had a negative effect.

These findings highlight the possibility of drawing erroneous conclusions resulting from not incorporating involvement, in conjunction with information accessibility and diagnosticity, in the study of the consumer external information search behavior.

The findings also imply that if practitioners aim to prime consumers to engage in external information search, they need to take into account that the effects of internal information's accessibility and diagnosticity on consumers' external search behavior may be different depending on their levels of involvement.

This study's results showed that without considering the moderating effect of involvement, spurious conclusions may be made about the relationships between accessibility and diagnosticity of internal and external information importance. This finding may explain the discrepancy between the accessibility/diagnosticity and ease-of-retrieval frameworks, thus enriching the literature.

This study aims to explore the perceptions of investors regarding the risks associated with funding renewable energy projects in India, as well as the various factors that influence these perceptions. The investigation is limited to debt providers and seeks to pinpoint the primary risks that bankers perceive and the drivers that shape these perceptions.

This study draws on interviews and surveys of Indian bank executives, investigating how finance providers perceive risks in the Indian context and the factors driving such perceptions. Qualitative interviews have been used for operationalizing “risk perception” within the renewable energy domain, followed by a quantitative survey and exploratory factor analysis.

The authors find that experience and capacity are the most important factors that account for 30% of the overall variance. The second factor, which accounts for 15% of the variance, includes the perceived risks in funding renewable energy projects as compared to infrastructure projects. Among individual risks, the authors find that bankers perceive technological risk to be the lowest (5%) and contractual and regulatory risks as the highest (66%) in renewable energy projects.

The study contextualizes risk perception toward renewable energy investments in the Indian context by drawing from the risk perception literature and qualitative interviews with senior bankers. It presents empirical evidence on the decision-making behavior of bankers, who are important stakeholders of the renewable energy ecosystem. The main limitation of the study is the relatively small sample, and generalizing the results to the broader population might require a larger sample. This will facilitate the use of confirmatory factor analysis and structural equation modeling, which can facilitate a more comprehensive understanding of risk perceptions in renewables financing.

Insights gained can be used to provide policy recommendations for improving the financing ecosystem of renewable energy projects. The research significantly contributes to the extant literature within the renewable energy financing domain for emerging economies.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.