Dunja Demirović Bajrami, Marija Cimbaljević, M. Petrović, Milan M. Radovanović, T. Gajić
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Binary genetic algorithms were used to segregate the subset of predictors that would be most suitable to describe variance in the outcome.\n\n\nFindings\nThe results showed that internal communication, incentive and reward systems, work support, work environment, openness and creative self-efficacy were the most important predictors of almost all the phases of green innovative behavior.\n\n\nOriginality/value\nThe research showed that a multidimensional approach in analyzing green innovative behavior is necessary as some factors can be significant or not so significant predictors. Acknowledging that innovation is a multistage process, entailing distinct activities and requiring varied individual behaviors to accomplish each task, amplifies the importance of this inquiry. Employees’ personal characteristics have direct impact on green innovative behavior in hospitality. Further, the results gave an insight into the possible mix of elements of internal marketing that can be used for boosting employees’ green innovative behavior in hospitality. This is important as implementing effective internal marketing practices empowers organizations to motivate employees to invest discretionary efforts.\n","PeriodicalId":7,"journal":{"name":"ACS Applied Polymer Materials","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Internal marketing and employees’ personality traits toward green innovative hospitality\",\"authors\":\"Dunja Demirović Bajrami, Marija Cimbaljević, M. Petrović, Milan M. Radovanović, T. 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Internal marketing and employees’ personality traits toward green innovative hospitality
Purpose
The current study aims to examine if the internal marketing and employees’ personal traits can predict their green innovative behavior at the workplace.
Design/methodology/approach
A survey was conducted with 683 frontline employees working in four- and five-star hotels in Serbia. Zero-order bivariate correlations among variables and linear multiple regression were conducted to predict green innovative behavior based on internal marketing, personality traits and psychological capital. Binary genetic algorithms were used to segregate the subset of predictors that would be most suitable to describe variance in the outcome.
Findings
The results showed that internal communication, incentive and reward systems, work support, work environment, openness and creative self-efficacy were the most important predictors of almost all the phases of green innovative behavior.
Originality/value
The research showed that a multidimensional approach in analyzing green innovative behavior is necessary as some factors can be significant or not so significant predictors. Acknowledging that innovation is a multistage process, entailing distinct activities and requiring varied individual behaviors to accomplish each task, amplifies the importance of this inquiry. Employees’ personal characteristics have direct impact on green innovative behavior in hospitality. Further, the results gave an insight into the possible mix of elements of internal marketing that can be used for boosting employees’ green innovative behavior in hospitality. This is important as implementing effective internal marketing practices empowers organizations to motivate employees to invest discretionary efforts.
期刊介绍:
ACS Applied Polymer Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics, and biology relevant to applications of polymers.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates fundamental knowledge in the areas of materials, engineering, physics, bioscience, polymer science and chemistry into important polymer applications. The journal is specifically interested in work that addresses relationships among structure, processing, morphology, chemistry, properties, and function as well as work that provide insights into mechanisms critical to the performance of the polymer for applications.