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The purpose of this paper is to argue that green hope (GH) and green organizational identification (GOI) play critical roles in transforming top management green commitment (TMGC) into desired employees task-related green behavior (TRGB) and voluntary workplace green behavior (VWGB) based on positive psychology.

The authors test the multilevel moderated mediation model by analyzing data collected from 491 hospitality employees and their direct supervisors in 103 teams. At Time 1, the authors conducted a survey of 905 team members to provide demographic information and evaluate TMGC, as well as their own GOI. At Time 2, the authors sent a follow-up questionnaire to employees who participated Time 1, asking them to evaluate their GH in the workplace. At Time 3, the authors sent questionnaires to the leaders of the respondents of T2 survey and invited them to evaluate TRGB and VWGB in the workplace.

The results show that TMGC facilitates two types of employees’ behaviors toward both TRGB and VWGB by enhancing hospitality employees’ GH. As a team-level variable, GOI has a positive moderating effect on the association between TMGC and GH. The authors discuss the theoretical implications as well as practical implications for managers seeking to promote sustainability in their hospitality industry.

This is one of the first empirical studies to investigate the mediating effects of a positive psychology variable, namely, GH – and the moderating effects of GOI on the relationship between TMGC and employee green behavior (EGB).

In the big data era, image compression is of significant importance in today’s world. Importantly, compression of large sized images is required for everyday tasks; including electronic data communications and internet transactions. However, two important measures should be considered for any compression algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen bases technique is applied at the first stage in which the average subspace is applied to each 3 × 3 block. Those blocks with the highest energy are replaced by a single value that represents the average value of the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression, it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to further increase the compression factor. The goal of using RLE is to enhance the compression factor without adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases technique, as described in the proposed algorithm, ensures high quality decompressed images and high compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance with other existing methods.