Although the extant studies had examined the impact of green marketing, limited research has focused on green marketing as an attempt of cleaner production. This paper contributes to green marketing and cleaner production literature by introducing "clean service marketing" through adaptation of cleaner production onto the expanded green service marketing mix (people, physical evidence and process). The study further contributes to the literature by investigating the possible influence of clean service marketing in providing health value, enhancing social-quality performance and good differentiation advantage. The authors adopted a mixed-method study by systematic review and survey questionnaire to collect data. A systematic review was conducted to address the research question "Do firms' green approaches provide health value to its stakeholder? While 101 sets of questionnaire were distributed to the managers of the selected three-to-five stars hotel and resort in Malaysia to confirm the proposed hypotheses. Partial Least Square-Structural Equation Modeling was employed for quantitative data analysis, and SmartPLS 3.2.8 software was performed to analyze the data obtained. The results of the synthesis analysis addressed the research question that firms or any practitioners by going green could either improved human's health or perceived health. The result of the quantitative analysis revealed that only the green process is positively related to social-quality performance. In contrast, green people, green physical evidence and green process were found all positively related to differentiation advantage. With regards, the authors strongly recommend hotel and resort firms taking green as a "clean" approach for hotels' post-pandemic recovery.
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method's suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman's rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique.•The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS.•Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making.•The maximum method (version I) or Jüttler's-Körth's method has an identical effect on TOPSIS results.