Hasil untuk "Business"

H. Snyder

Abstract Knowledge production within the field of business research is accelerating at a tremendous speed while at the same time remaining fragmented and interdisciplinary. This makes it hard to keep up with state-of-the-art and to be at the forefront of research, as well as to assess the collective evidence in a particular area of business research. This is why the literature review as a research method is more relevant than ever. Traditional literature reviews often lack thoroughness and rigor and are conducted ad hoc, rather than following a specific methodology. Therefore, questions can be raised about the quality and trustworthiness of these types of reviews. This paper discusses literature review as a methodology for conducting research and offers an overview of different types of reviews, as well as some guidelines to how to both conduct and evaluate a literature review paper. It also discusses common pitfalls and how to get literature reviews published.

Gang Wang, A. Gunasekaran, E. Ngai et al.

The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) – that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.

A. Murray, Keith R. Skene, Kathryn Haynes

B. Parmar, R. Freeman, J. Harrison et al.

J. Johanson, J. Vahlne

M. Dodgson, L. Xue

D. Teece

This paper draws on the social and behavioral sciences in an endeavor to specify the nature and microfoundations of the capabilities necessary to sustain superior enterprise performance in an open economy with rapid innovation and globally dispersed sources of invention, innovation, and manufacturing capability. Dynamic capabilities enable business enterprises to create, deploy, and protect the intangible assets that support superior long- run business performance. The microfoundations of dynamic capabilities—the distinct skills, processes, procedures, organizational structures, decision rules, and disciplines—which undergird enterprise-level sensing, seizing, and reconfiguring capacities are difficult to develop and deploy. Enterprises with strong dynamic capabilities are intensely entrepreneurial. They not only adapt to business ecosystems, but also shape them through innovation and through collaboration with other enterprises, entities, and institutions. The framework advanced can help scholars understand the foundations of long-run enterprise success while helping managers delineate relevant strategic considerations and the priorities they must adopt to enhance enterprise performance and escape the zero profit tendency associated with operating in markets open to global competition. Copyright  2007 John Wiley & Sons, Ltd.

C. Prahalad, G. Hamel

J. Liker

E. Penrose

Michael E. Porter, M. Kramer

T. Davenport

C. Grönroos

Bernard J. Jaworski, Ajay K. Kohli

P. Senge

J. C. Henderson, N. Venkatraman

M. Abramovitz, Moses Abram Ovitz, John N. Jacobson

Anh Nu Nguyet Nguyen, Ninh Van Nguyen

Purpose: The objective of this study is to explore and comprehend the factors from the perceived environment that impact travellers' attitudes and trust in agritourism at farms integrated with aquaculture, which have been creatively adapted for tourism purposes. These findings contribute to understanding how agritourism fosters rural innovation and sustainable development by transforming traditional agricultural practices into diversified tourism experiences. The findings of the study could demonstrate that certain outcomes play a crucial role in the successful innovation of rural areas. Methodology/design/approach: The study extensively utilized the Theory of Planned Behavior framework to develop its measurement constructs. Data collection occurred in regions where tourists frequented farms that combine aquaculture with traditional farming practices, yielding a total sample size of 332 respondents. The data were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, employing the SmartPLS software version 4.0.9.2. Results: The results identified factors perceived environmentally positive influence on personal perception. Attitude and trust were found to mediate the relationships between perceived environment and revisit intention, with the mediating effect of attitude being stronger than that of trust. Originality of the research: Visitor attitudes significantly determine the innovation from making farms, orchards, aquaculture areas to the experiential tourism business. Successful innovations, such as enhancing rural incomes and sustaining agricultural livelihoods through agritourism transformation, are significantly driven by positive visitor perceptions and trust.

Joel Victor Dossa, Chiagoziem C. Ukwuoma, Dara Thomas et al.

This study investigates the nexus between ESG disclosure and firm performance using advanced machine learning models (MLs) to capture complex, non-linear interactions. Analyzing data from Chinese A-share firms (2012–2022), it employs Explainable AI (XAI) tools such as SHAP, heat maps, and Williams plots to enhance model transparency and interpretability. Among several models, the Extra Trees model demonstrated the best predictive performance, revealing that ESG disclosure positively correlates with firm performance, with environmental disclosure exerting the strongest influence. Policymakers are urged to promote standardized, transparent ESG disclosures, particularly focusing on environmental practices while addressing greenwashing to enhance credibility. Investors can prioritize firms with strong environmental practices and use predictive models to refine decision-making. Corporate managers are encouraged to embed sustainability into long-term strategies and utilize ML techniques for improved governance. The study contributes by showcasing the utility of MLs in exploring ESG-performance relationships, offering actionable insights for stakeholders, and providing a foundation for future research. Researchers are encouraged to investigate non-linear ESG impacts across diverse contexts, using broader samples and incorporating market-based measures and ESG rating agencies to improve generalizability. This approach advances understanding of ESG's role in driving firm performance while addressing methodological gaps.

Devi Nurmalasari, Dominicus Savio Priyarsono, Linda Karlina Sari

Integrity is a cornerstone of good governance; yet, its realization remains uneven across various levels of government in Indonesia. This study aims to compare the influence of integrated internal control system components—risk management, internal audit, internal control systems, and corruption control—on the integrity of central and regional government agencies. A quantitative approach is employed using Spearman’s rank correlation and panel data regression analysis on data from 42 central and 491 regional agencies collected between 2022 and 2023. Model selection is based on the Chow, Hausman, and Breusch–Pagan tests. The results show that in regional agencies, risk management, internal control systems, and corruption control significantly enhance integrity, while internal audit does not. Conversely, none of the control components show a significant effect on integrity in central agencies, indicating a gap between system maturity and actual governance outcomes. These differences reflect contextual challenges, including bureaucratic complexity, limited leadership engagement, and a compliance-oriented implementation approach. The study concludes that tailored strategies are needed to strengthen internal audits in regional agencies and to move beyond procedural compliance in central institutions. The study offers practical implications for improving public governance and contributes novel insights through its comparative approach across government levels.