Two research questions are identified and discussed. Relevant factors that influence the customer preferences were selected as research objects, data collection was based on 348 valid questionnaires, SPSS software was used for data analysis by the means of the multivariate logistic research model. Customer intentions include delivery payment, delivery time, food quality and brand trust. Customer reviews are related to customer preferences. The delivery payment is the most important factor when customers use food delivery service, but different groups of people have different tendency compared with their counterparts. All variables are designed based on baseline categories; the outcome of the model is only significant while comparing two groups of variables. Multivariate logistic research model is used to find customer preferences under the different characteristic of customer groups based on questionnaires and tries to forecast the possibility of the tendency of one targeting customer group in Suzhou Industrial Park. This research conduct a questionnaire on the Suzhou industry park, the respondents are mainly students and white collars customers, the characteristics of respondents are typical in this area.

Big data technology has brought about the establishment of transportation network companies (TNCs), such as Uber in the USA, and Didi in China, who provide the ride hailing services (RHS's) which allows the individuals to act as independent contractors serving customers via a smartphone app. The RHS system installed gigantic databases which can store enormous data and its equipment can collect various kinds of data at any time. Additionally, data analysis technologies in TNCs such as behavioral analysis, heatmap optimization surge pricing and the automatic order assignment mechanisms, can make the RHS more efficient. However, along with achievements big data technology offers, we are struggling towards the ethical problems, including privacy, inequity and safety.

With the development of big data technology, such as data mining and data matching, many industries have started a revolution, including medical field. Big data not only strengthens the accuracy of medical diagnosis, but it also enhances the efficiency of the entire medical system and relevant medical staff. Additionally, with the rethinking of innovation, the application of wearable intelligent device, RFID technology and sensor technology play positive roles in promoting medical interaction between hospital system and wearer. Smart medical provides effective methods for individual health management and promotes the progress of medical information. However, there are also some inevitable ethical problems, e.g., the leakage of privacy information, which cannot be avoided to some extents. The authors recommend some suggestions to reduce the possibilities of ethical problems happened during the data flow process.

With the development of Internet of Things (IoT) technologies, the medical field has begun to use this technology to better serve the society. The users’ data can be accurately collected and analyzed, and people can get the same quality of medical services without shuttling back and forth between hospitals and their homes. Smart healthcare not only reduces the social burden, but it also lowers the financial burden on end users. However, the collection and upload of massive data still have concerning data security risk, which may lead to various ethical problems and endanger the vital interests of users. By analyzing the causes of ethical issues, we can provide suggestions on what actions users and developers should take and the role that governments play. How to balance the user experiences and ethical security is always a hot topic. In this paper, we present our review and recommendations to balance both the use of IoT technologies, smart healthcare and ethics for delivering smart medical services.

Knowledge hiding is widely considered a counter-productive workplace behavior that can hinder the employees' creativity and have a negative impact on performance. Although companies are prone to encourage knowledge sharing practices, employees are inclined to hide their knowledge – tacit and explicit. Often this happens in research and development (R&D) process where team members may distrust each other or intentionally are not hostile in sharing knowledge. The phenomenon of knowledge hiding has increased the interest in researchers who have explored it in different views, there has been little research into the antecedents of knowledge hiding and the social factors that trigger the relate behavior. In this vein, the current study seeks to analyze antecedents and social factors through the lens of the theory of planned behavior as the guiding theory in an in-depth qualitative research. Specifically, knowledge hiders' attitudes, subjective norms and their perceived behavioral control over the knowledge hiding along with the cultural dimensions of 15 international R&D teams are investigated. Although exploratory, the study reveals the fact that cultivating an environment of collaboration and knowledge sharing is beneficial as it removes the organizational foundation of knowledge hiding, which is more likely to result in increased innovation within the whole organization. A comprehensive theoretical framework of knowledge hiding is proposed, and its implications on theory and practice are discussed with the aim of nudging further explorations on the topic.