Improving the Prediction of Commitment and Innovative Work Behavior from Climate for Innovation Perceptions: An Application of Latent Profile Analysis

Psychological Climate;Attraction-Selection-Attrition;Latent Profile Analysis;Organizational Climate

Cantwell, April Renee ; Mark A. Wilson, Committee Member,Lynda Aiman-Smith, Committee Member,S. Bartholomew Craig, Committee Member,Samuel B. Pond, III, Committee Chair,Cantwell, April Renee ; Mark A. Wilson ; Committee Member ; Lynda Aiman-Smith ; Committee Member ; S. Bartholomew Craig ; Committee Member ; Samuel B. Pond ; III ; Committee Chair

University:North Carolina State University

关键词: Psychological Climate;    Attraction-Selection-Attrition;    Latent Profile Analysis;    Organizational Climate;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/6254/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

In this research study, I conceptualized climate for innovation perceptions as representing subjective interpretations of the environment, and also as representing a complex and dynamic interaction between people and environments. Simple variable approaches and, as demonstrated by Young and Parker (1999), simple aggregation of climate survey scores to formal organizational groups seems inadequate to represent this complexity. The use of clustering techniques to identify homogeneous groups with regard to perceptions of climate (e.g., Schneider & Reichers, 1983; Mathisen & Einarsen, 2004; Joyce & Slocum, 1984) is an approach that can represent data complex interactions of people and environments. Researchers have not yet determined which clustering techniques best account for the complex influences on perceptions of climate or best predict associated organizational outcomes. Latent profile analysis (LPA) is a clustering technique that may move the climate research forward. I used LPA to classify individuals by their climate for innovation perceptions and simultaneously to assess the relative contributions of situational and individual difference covariates, including company membership, functional membership, organizational level, and organizational tenure. Latent class membership was used to predict affective, normative, and continuance (ANC) commitment to the organization; ANC commitment to innovation, creative innovative work behavior (IWB), and implementation IWB to determine if latent class membership predicted these outcomes beyond the contribution climate for innovation perceptions. The archival dataset included 1,891 individual respondents from four high-technology firms. Only 383 cases provided commitment and IWB outcome data for the predictive study. The Innovation-Capacity Climate Survey (ICCS) measured nine dimensions of the climate for innovation, including Meaningful Work, Risk Taking, Customer Orientation, Agile Decision Making, Business Intelligence, Open Communication, Empowerment, Business Planning, and Learning Organization (Aiman-Smith, Goodrich, Roberts, & Scinta, 2005). A six-factor adaptation of Meyer and Herscovitch’s (2001) ANC commitment model measured ANC commitment to the organization and ANC commitment to innovation. A two-factor adaptation of Dorenbosch, van Engen, and Verhagen’s (2005) IWB scales measured Creative and Implementation IWB. The nine ICCS scales, four of six commitment scales, and both IWB scales demonstrated acceptable confirmatory factor analysis fit, maximal and internal consistency reliability, and expected factor interrelationships. After I identified three viable latent class solutions using LPA, I used multivariate analysis of variance (MANOVA) to examine the relationships among situational and individual difference covariates. I concluded that individual differences contributed more to perceptions of climate for innovation than did situational variables. I next used multivariate analysis of covariance (MANCOVA) to test whether latent class membership predicted commitment and IWB, with ICCS scores entered as covariates. For two latent class solutions, class membership predicted normative commitment to innovation; for one solution, class membership predicted IWB. Power and effect sizes for all of these analyses were low. Finally, I tested the hypotheses that climate perceptions decrease with increasing organizational tenure at the group and company level. Neither test was statistically significant but, at the company level, effect sizes were moderate and sample size was small. In sum, I sought to explore climate perceptions as a complex interaction of situation and individual differences. Although not conclusively so, I demonstrated that latent class membership was related to individual differences and situational factors, and that it predicted commitment and IWB. Future research should include person and situation variables, and models should be designed to assess the complex interactions among these variables. LPA modeling is a promising technique for understanding climate in organizations and important organizational outcomes consistent with my viewpoint.