Institutional-based antecedents and performance outcomes of internal and external green supply chain management practices

doi:10.1016/J.PURSUP.2012.12.001

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Institutional-based antecedents and performance outcomes of internal and external green supply chain management practices

Journal Article•DOI•

Institutional-based antecedents and performance outcomes of internal and external green supply chain management practices

Qinghua Zhu¹, Joseph Sarkis², Kee-hung Lai³•Institutions (3)

Dalian University of Technology¹, Saint Petersburg State University², Hong Kong Polytechnic University³

01 Jun 2013-Journal of Purchasing and Supply Management (Pergamon)-Vol. 19, Iss: 2, pp 106-117

TL;DR: Wang et al. as mentioned in this paper developed a theoretical model on the different types of institutional pressures motivating manufacturing enterprises to pursue green supply chain management (GSCM) practices and commensurate performance outcomes.

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About: This article is published in Journal of Purchasing and Supply Management.The article was published on 2013-06-01. It has received 778 citations till now. The article focuses on the topics: Supply chain management & Institutional theory.

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Citations

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The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

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Paul DiMaggio, Walter W. Powell

01 Jan 2008

TL;DR: In this article, the authors argue that rational actors make their organizations increasingly similar as they try to change them, and describe three isomorphic processes-coercive, mimetic, and normative.

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Abstract: What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

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2,134 citations

Journal Article•DOI•

A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements

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E. Manavalan¹, Kandasamy Jayakrishna¹•Institutions (1)

VIT University¹

01 Jan 2019-Computers & Industrial Engineering

TL;DR: In this article, a comprehensive study on various factors, that affects the sustainable supply chain were analyzed and the results recorded, based on the review, a framework for assessing the readiness of supply chain organization from various perspectives has been proposed to meet the requirements of the fourth Industrial Revolution.

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526 citations

Journal Article•DOI•

Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices

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Abdul-Nasser El-Kassar¹, Sanjay Kumar Singh²•Institutions (2)

Lebanese American University¹, Abu Dhabi University²

01 Jan 2018-Technological Forecasting and Social Change

TL;DR: In this article, the authors developed and tested a holistic model that depicts and examines the relationships among green innovation, its drivers, as well as factors that help overcome the technological challenges and influence the performance and competitive advantage of the firm.

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515 citations

Journal Article•DOI•

Sustainable supply chain management: framework and further research directions

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Rameshwar Dubey¹, Angappa Gunasekaran², Thanos Papadopoulos³, Stephen J. Childe⁴, K. T. Shibin¹, Samuel Fosso Wamba⁵ - Show less +2 more•Institutions (5)

Symbiosis International University¹, University of Massachusetts Dartmouth², University of Kent³, University of Plymouth⁴, NEOMA Business School⁵

20 Jan 2017-Journal of Cleaner Production

TL;DR: In this article, the authors argue for the use of Total Interpretive Structural Modeling (TISM) in sustainable supply chain management (SSCM) and propose a framework that extrapolates SSCM drivers and their relationships.

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422 citations

Cites background from "Institutional-based antecedents and..."

...Research on the role of environmental collaboration has mainly focused on its antecedents and performance implications (e.g. Zhu et al., 2013; Grekova et al., 2015)....
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...…Holt, (2005); Zailani et al. (2012); Wang and Sarkis (2013); Ortas et al. (2014); Wang and Sarkis (2013); Mitra & Datta (2014) Green Product Design Zhu et al. (2013); Linton et al. (2007); Dangelico & Punjari (2010); Sharma et al. (2010); Alblas et al. (2013); Driessen et al. (2013) M AN US CR IP…...
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...Some of the scholars in their works have also noted that green product design has significant positive influence on sustainable business development (see Linton et al. 2007; Dangelico & Punjari, 2010; Sharma et al. 2010; Alblas et al. 2013; Driessen et al. 2013; Zhu et al. 2013)....
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...(2014) Institutional Pressures Ketokivi & Schroeder, (2004); Zhu et al. (2005); Zhu et al (2007a); Jayaraman et al. (2007); Ketchen and Hult, (2007); Liang et al.(2007); Cai et al. (2010); Liu et al.(2010); Sarkis et al.(2011); Kang et al. (2012); Law & Gunasekaran, 2012; Bhakoo and Choi, (2013); Kauppi, (2013); Coyle et al. (2014); Tseng and Hung (2014); Dubey et al. (2015) Social Values & Ethics Roberts, (2003); Beamon (2005); Drake & Schlachter, (2008); Sarkis et al. (2010); Carter & Jennings (2002a, b); Hoejmose et al. (2013) Gold et al. (2010); Rokka & Uusitalo (2008); Mueller et al., (2009); Gloss et al.(2011); Gunasekaran & Spalanzani, (2012); Eriksson et al. (2015) Corporate Strategy & Commitment Carter & Dresner, (2001); Griffiths & Petrick, (2001); Narasimhan & Das (2001); McAfee et al. (2002); Mello & Stank (2005); Day & Lichtenstein (2006); Liang et al. (2007); Gattiker & Carter, (2010); Hofmann (2010); Dey et al. (2011); Law & Gunasekaran, (2012); Abdulrahman et al. (2014); Foerstl et al. (2015); Jabbour & Jabbour, (2015) Economic stability Rao & Holt, (2005); Zailani et al. (2012); Wang and Sarkis (2013); Ortas et al. (2014); Wang and Sarkis (2013); Mitra & Datta (2014) Green Product Design Zhu et al. (2013); Linton et al. (2007); Dangelico & Punjari (2010); Sharma et al. (2010); Alblas et al. (2013); Driessen et al. (2013) M AN US CR IP T AC CE PT ED...
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...Using the symbols i and j to denote columns and rows, the relationships between nodes are shown as follows: V: if i leads to j but j doesn’t lead to i A: if i doesn’t lead to j but j leads to i X: if i and j lead to each other O: if i and j are not related each other Table 2: Structural Self-Interaction Matrix (SSIM) V12 V11 V10 V9 V8 V7 V6 V5 V4 V3 V2 V1 V1 O O A V A A A X X A A X V2 A A A O O X A V O V X V3 O O A A X A A V A X V4 A O A O V V V V X V5 A V A A A A A X V6 O O A O V A X V7 A O O O V X V8 O O A A X V9 X A A X V10 V A X M AN US CR IP T AC CE PT ED V11 X X V12 X Identified variables of SSCM: V1 - Economic stability, V2 - Green Product Design, V3 - Green warehousing, V4 - Strategic supplier collaboration, V5 - Environment conservation, V6 – Continuous improvement, V7- Enabling Information Technologies,V8 - Logistics Optimization,V9 – Internal Pressures, V10 - Institutional Pressures, V11-Social Values & Ethics, V12- Corporate strategy & commitment....
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Journal Article•DOI•

What drives eco-innovation? A review of an emerging literature

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Jana Hojnik¹, Mitja Ruzzier¹•Institutions (1)

University of Primorska¹

01 Jun 2016-Environmental innovation and societal transitions

TL;DR: An overview of the emerging literature on the drivers of eco-innovation can be found in this paper, where the main contribution lies in separating the drivers associated with the phases of development and diffusion and in identifying particular drivers based on different ecoinnovation types.

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Abstract: This paper provides an overview of the emerging literature on the drivers of eco-innovation. Its main contribution lies in separating the drivers associated with the phases of development and diffusion and in identifying particular drivers based on different eco-innovation types. We find that research in this area primarily adopts the resource-based and institutional theories as its theoretical foundations and that the prevailing effects identified are those of regulations and market pull factors. Moreover, product eco-innovation, process eco-innovation, organizational eco-innovation, and environmental R&D investments seem to be driven by common drivers, such as regulations, market pull factors, EMS, and cost savings, as well as to be positively associated with company size. The majority of the studies in our literature review employ a quantitative research methodology and focus on the diffusion stage of eco-innovation. We end with providing a synthesis of drivers of companies’ eco-innovation and directions for future research.

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396 citations

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References

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Open Access

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Journal Article•DOI•

Common method biases in behavioral research: a critical review of the literature and recommended remedies.

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Philip M. Podsakoff¹, Scott B. MacKenzie¹, Jeong-Yeon Lee¹, Nathan P. Podsakoff²•Institutions (2)

Indiana University¹, University of Florida²

01 Oct 2003-Journal of Applied Psychology

TL;DR: The extent to which method biases influence behavioral research results is examined, potential sources of method biases are identified, the cognitive processes through which method bias influence responses to measures are discussed, the many different procedural and statistical techniques that can be used to control method biases is evaluated, and recommendations for how to select appropriate procedural and Statistical remedies are provided.

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Abstract: Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

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52,531 citations

"Institutional-based antecedents and..." refers methods in this paper

...To avoid ‘item characteristic’ effects as one of the key causes for common method bias (Podsakoff et al., 2003) due to ambiguous items that can result in unreliable answers, a pretest for the survey questionnaire (measurement) items were carried out for evaluating the theoretical constructs on the…...
[...]
...To avoid ‘item characteristic’ effects as one of the key causes for common method bias (Podsakoff et al., 2003) due to ambiguous items that can result in unreliable answers, a pretest for the survey questionnaire (measurement) items were carried out for evaluating the theoretical constructs on the implementation of GSCM and its antecedents and performance outcomes....
[...]

Journal Article•DOI•

Multivariate Data Analysis

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Joseph F. Hair

01 Jan 1973

TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.

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Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

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37,124 citations

Book Chapter•DOI•

The iron cage revisited institutional isomorphism and collective rationality in organizational fields

[...]

Paul DiMaggio, Walter W. Powell

01 Apr 1983-American Sociological Review

TL;DR: In this paper, the authors argue that rational actors make their organizations increasingly similar as they try to change them, and describe three isomorphic processes-coercive, mimetic, and normative.

...read moreread less

32,981 citations

Journal Article•DOI•

Multivariate data analysis

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Joseph F. Hair

01 Mar 1972-Contemporary Sociology

TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.

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Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats

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Psychometric Theory (2nd ed), New York: McGraw-Hill.

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JC Nunnally

01 Jan 1978

13,810 citations

"Institutional-based antecedents and..." refers background in this paper

...In sum, the overall reliability for the study constructs on GSCM pressures/drivers, practices, and performance can be considered satisfactory (Nunnally, 1978; Litwin, 1995)....
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