Dynamic interplay of environmental sustainability and corporate reputation: a combined parametric and nonparametric approach

被引:11
作者
Kwon, He-Boong [1 ]
Lee, Jooh [2 ]
Choi, Laee [1 ]
机构
[1] Colorado State Univ, Malik & Seeme Hasan Sch Business, 2200 Bonforte Blvd, Pueblo, CO 81001 USA
[2] Rowan Univ, William G Rohrer Coll Business, 201 Mullica Hill Rd, Glassboro, NJ 08028 USA
关键词
Corporate reputation; Neural network; Sustainability; Tobin's Q; ARTIFICIAL NEURAL-NETWORKS; SOCIAL-RESPONSIBILITY; FINANCIAL PERFORMANCE; FIRM PERFORMANCE; COMPETITIVE ADVANTAGE; MANUFACTURING FIRMS; EMPIRICAL-ANALYSIS; GREEN INNOVATION; IMPACT; MARKET;
D O I
10.1007/s10479-021-04331-6
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
For decades, research on the relationship between environmental sustainability and firm performance focused on determining whether the effect of the former on the latter was positive or negative. That potentially controversial approach had driven mainstream research to ask two focal questions: Does it pay to be green? and Does it cost to be green? Recent papers have explored a curvilinear effect (i.e., U-shaped and inverted U-shaped) by tackling another question: When does it pay to be green? These efforts, however, have either oversimplified the complex linkage between these two things into a linear relationship or confined the complexity within a formatted frame. This paper overcomes these shortfalls by exploring the holistic effect of environmental sustainability on firm performance, contingent upon corporate reputation. In conjunction with direct effect analysis, this paper uniquely explores the synergistic effect of environmental sustainability and corporate reputation on performance. In addition to detecting the nonlinear synergy pattern, we found that the synergy effect is asymmetric. Specifically, the effect is complementary for low-reputation firms but substitutive for high-reputation firms. In addition to its theoretical contributions and pragmatic implications, this paper uniquely presents PROCESS-Neural network as a promising analytic paradigm.
引用
收藏
页码:687 / 719
页数:33
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