Recommender systems assist users to make decisions among a huge volume of options. Accuracy-oriented recommender systems focus on the prediction power of algorithms and neglect that users may appreciate diverse and novel recommendations in real-world scenarios. Thus, this paper proposed a multicriteria recommendation model that can optimize the recommendation accuracy, diversity, novelty, and individual tendency simultaneously. Additionally, a new multiobjective bacterial foraging optimization method is proposed to improve its searching capability and the performance of recommendation model. The proposed optimization-based multicriteria recommendation algorithm is compared with existing methods on both benchmark functions and real-world data sets. The results demonstrate that the proposed algorithm is superior to other recommendation algorithms in most cases. This study provides insights in recommendation system design and draws scholarly attention to the optimization-based recommendation strategy.

The emerging technological trends have impacted both business firms and nonprofit organizations (NPO). For both of them to adopt emerging technological applications, the primary prerequisite is the acquisition of IS capabilities, which may not be challenging for business firms, but NPOs. In this paper, a case of academic-community collaboration between the Hong Kong Polytechnic University (HK PolyU) and the Scout Association of Hong Kong (SAHK) is presented. The collaboration involves some students' capstone projects with Information Systems (IS) background to develop a range of technological applications in response to the training and activities of SAHK. From this case, several theoretical implications of IS capabilities and academic-community collaboration are highlighted, as well as practical implications for academics and NPOs.

Sustainability issues have been seen as a promising paradigm for achieving a better future. Firms in the logistics service sector are still lacking clear value propositions on sustainable development. While many organizations publish their mission statements publicly as kinds of public information, reviewing mission statements is an appropriate means to evaluate an organization's strategy. This study focuses on the public information such as mission statements of the top 50 global 3-PL companies and the relevant sustainable development. A comprehensive content analysis identified four major content dimensions of mission statements relating to sustainability development. The dimensions are driving forces, approaches, responsibility to stakeholders, and competitive values. This paper offers a good methodological reference for researchers or practitioners managing the public information of organizations. Network analysis reveals that the location of companies has a limited effect on their mission and strategy as they all provide global service.

This paper presents an empirical study on how knowledge management practices and innovation sources affect product innovation performance, among the 152 manufacturers in the low- and high- tech industries in China. The results indicate that external innovation sources are positively correlated with innovation activities and new product performance. Intellectual Property (IP) and knowledge management practices (KMP) are positively correlated with innovation activities, and KMP is positively correlated with innovation sources. The dual effect of KMP shows its indispensable effect on the new product development for both high-tech and low-tech firms, but for low-tech firms, such effect is relatively weak. This empirical study shows that IP management is critical to high-tech but not low-tech firms. We also found that, for innovation activities, low-tech depends on the external sources of innovation whilst high-tech firms do not.

The global wine-making industry has faced challenges due to the increasing demands of consumers, particularly in emerging markets such as China, Brazil, India, and Russia. Controlling the quality during wine production is one of the key challenges faced by global winemakers to produce wine with appropriate sensorial properties tailored to specific markets. The wine production quality is constituted from a number of environmental factors such as climate, soil, and temperature, which affect the sensorial properties and the overall quality. This paper proposed a rule-based quality analytics system (RBQAS) to capture physicochemical data during wine production and to investigate the hidden patterns from the data for quality prediction. It consists of IoT for data capture on a real-time basis, followed by association rule mining to identify relationships between sensorial and physicochemical properties of wine.

The global wine-making industry has faced challenges due to the increasing demands of consumers, particularly in emerging markets such as China, Brazil, India, and Russia. Controlling the quality during wine production is one of the key challenges faced by global winemakers to produce wine with appropriate sensorial properties tailored to specific markets. The wine production quality is constituted from a number of environmental factors such as climate, soil, and temperature, which affect the sensorial properties and the overall quality. This paper proposed a rule-based quality analytics system (RBQAS) to capture physicochemical data during wine production and to investigate the hidden patterns from the data for quality prediction. It consists of IoT for data capture on a real-time basis, followed by association rule mining to identify relationships between sensorial and physicochemical properties of wine.