Purpose – Blog is a web-based social activity that has become mainstream media. The purpose of this paper is to develope a measurement instrument for assessing blog service innovation, which social media services providers and bloggers can use to promote blog functions and to ensure high quality blog services. Design/methodology/approach – The study adopted both qualitative and quantitative research methods and performed four steps of scale development, including item generation and item pooling, pretest and initial purification, scale refinement, and scale validation and critical item analysis. Findings – From a user-oriented service perspective, the major findings of the study were the 18 measurement items for blog service innovation scale (BLOG-S-INNO scale), which was derived from the innovative blog cognition of blog users. One critical factor in the BLOG-S-INNO scale was further identified to effectively predict outcomes of blog service innovation in social media services. Research limitations/implications – Management at social media services providers can apply the BLOG-S-INNO scale as a diagnostic tool to assess organizational innovation capabilities in relation to blog services, and to link their innovation strategies with the innovation experiences of bloggers improving bloggers’ affection. The findings of this study also make it possible to offer recommendations to help bloggers improve service innovation to increase the experience and preference of blog browsers. Originality/value – The study used qualitative research methods to construct a pool of items for measuring blog service innovation. Furthermore, the paper conducted quantitative research methods to develop a new blog service innovation scale and analyzed the key indicators of blog service innovation.

Most recommendation systems face challenges from products that change with time, such as popular or seasonal products, since traditional market basket analysis or collaborative filtering analysis are unable to recommend new products to customers due to the fact that the products are not yet purchased by customers. Although the recommendation systems can find customer groups that have similar interests as target customers, brand new products often lack ratings and comments. Similarly, products that are less often purchased, such as furniture and home appliances, have fewer records of ratings; therefore, the chances of being recommended are often lower. This research attempts to analyze customers' purchasing behaviors based on product features from transaction records and product feature databases. Customers' preferences toward particular features of products are analyzed and then rules of customer interest profiles are thus drawn in order to recommend customers products that have potential attraction with customers. The advantage of this research is its ability of recommending to customers brand new products or rarely purchased products as long as they fit customer interest profiles; a deduction which traditional market basket analysis and collaborative filtering methods are unable to do. This research uses a two-stage clustering technique to find customers that have similar interests as target customers and recommend products to fit customers' potential requirements. Customers' interest profiles can explain recommendation results and the interests on particular features of products can be referenced for product development, while a oneto-one marketing strategy can improve profitability for companies. q

This study explores the role of deep learning technology in the sustainable development of the music production industry. This article surveys the opinions of Taiwanese music creation professionals and uses partial least squares (PLS) regression to analyze and elucidate the importance of deep learning technology in the music production industry. We found that deep learning cannot replace human creativity, but greater investment in this technology can improve the quality of music creation. In order to achieve sustainable development in the music production industry, industry participants need to awaken consumers’ awareness of music quality, actively enhance the unique value of their art, and strengthen cooperation between industries to provide a friendly environment for listeners.

Against the background of sustainable development, green building practices could be part of the strategy for solving environmental and energy problems in developing countries. The aim of this paper is to explore a system for the assessment of green buildings in China that provides the government and stakeholders with ways to improve their strategies for green building development. We apply a hybrid model, developed by integrating the Decision-Making Trial and Evaluation Laboratory and Analytical Network Process (called DANP) method, to build an influential network relationship map (INRM) between assessment systems and to derive the criterion weights. The INRM and derived weights can help us to understand this complex assessment system and to set improvement priorities for green building development. The results demonstrate that indoor environment, materials, and smart facilities are the top three critical factors for green building evaluation. Finally, we discuss some management implications based on an actual case study with solutions provided using this model.

China is pushing universities to implement reforms in order to achieve the sustainable development goals, but with the development level of teachers becoming the key restricting factor. In this sense, teacher evaluation and improvement act as positive factors for China to achieve the 2030 sustainable development goals. Previous studies on teacher evaluation have usually assumed that the relationship between the evaluation criteria is independent, with the weights of each standard derived from this assumption. However, this assumption is often not in line with the actual situation. Decisions based on these studies are likely to waste resources and may negatively impact the efficiency and effectiveness of teachers’ sustainable development. This study developed an integrated model for the evaluation and improvement of teachers based on the official teacher evaluation criteria of China’s International Scholarly Exchange Curriculum (ISEC) programme and a multiple criteria decision-making methodology. First, a decision-making trial and a laboratory-based analytical network process were used to establish an influential network-relation diagram (INRD) and influential weights under ISEC standards. Next, an important performance analysis was used to integrate the weight and performance of each standard to produce a worst-performance criterion set for each university teacher. Finally, the worst performance set used an INRD to derive an improvement strategy with a cause–effect relationship for each teacher. This study chose a Chinese university that has implemented teaching reform for our case study. The results show that our developed model can assist decision-makers to improve their current evaluations of teachers and to provide a cause–effect improvement strategy for education reform committees and higher education institutions.