International Journal of Information Systems and Change Management (3 papers in press)

Regular Issues

• Online impulse buying of organic food: a moderated (website personality) mediation (social appeal) process  
  by Anum Tariq, Changfeng Wang, Yasir Tanveer, Umair Akram, Muhammad Bilal 
  Abstract: While existing literature has addressed the antecedents of impulsive buying behavior, this research study investigates the impulse buying of organic food through mediating effect of social appeal on the relationship between consumer attitude and online impulse buying behavior and whether this mediated relationship is moderated by website personality. In total, 600 respondents were asked to fill online survey questionnaire, however 423 valid responses were collected in Beijing, China. Data were collected in a period of three months from October-December 2017 by survey questionnaire place on frequently used social media platform like Wechat, QQ and Sina Weibo. Results revealed a significant full mediation in a positive relationship between customer attitude and online impulse buying of organic food. This mediating effect of social appeal is further moderated by website personality that advocates that the mediating effect of social appeal is stronger for online browsers inducing more impulsive buying decisions. Results instigate online marketing professionals to review their tactics to deal with modern consumers by developing websites with social learning mechanism, user-friendlier and visually appealing to push organic intake. Results propose actions to enhance organic consumption among modern consumers and contribute to the literature on social factor adoption resulting in a more nuanced deliberation on the online impulse buying of organic food.rn
  Keywords: social appeal; website personality; online impulse buying behaviour; organic food rn.
  
  
• Binomial logit regression and centralized agent stochastic optimization for privacy preserved load balancing in cloud  
  by Jawahar Muthusamy, Sabari A, S. Monika 
  Abstract: Load balancing and privacy preservation of data plays an important role in cloud. Few research works have been designed to perform load balancing on cloud server and to preservethe data privacy. However, performance of conventional technique was not improved. In order to overcome such limitation, a Binomial Logit Privacy Preserved Load balancing (BLPPLB) Technique is proposed. The BLPPLB Technique is designed for improving the cloud security with higher data privacy level and load balancing efficiency. At first the request is sent from user to cloud server. BLPPLB Technique carried out binomial logit authentication with help of regression coefficient for each user requests based on user behaviour on cloud. Then, BLPPLB Technique finds the intruder attacks and authorized users in cloud. After performing authentication, BLPPLB Technique allows only authorized users to access data from cloud server for preserving data from intruders. During data accessing process, load balancing is performed through selecting optimal server among multiple servers for each user requests based on objective function using Centralized Agent Based Stochastic Local Search to provide the requested services. The experimental evaluation of BLPPLB Technique is conducted on metrics such as load balancing efficiency, intruder detection rate, data confidentiality level and data accessing time with respect to number of users. The experimental result shows that the BLPPLB Technique is able to increase the load balancing efficiency and data confidentiality level in cloud when compared to state-of-the-art works.
  Keywords: Binomial Logit Regression; Centralized Agent; Cloud Server; Intruder Attacks; Load Balancing; Objective Function; Stochastic Local Search; User Request.
  
  
• A Human Vision System-based Approach to Improve Computing Efficiency in Information Systems  
  by Jianxun Li, Jim Q. Chen, Weiqian Li 
  Abstract: While a lot of research has been done in system design, reducing computational complexity is still a core problem in information systems. In order to break through the bottleneck of computing efficiency, this paper draws on the powerful processing capability of the Human Vision System (HVS) to propose a pixel domain computing framework. It converts the original problem into pixel operations, and the pixel is taken as the basic unit to change the construction process of data analysis. We use the framework to significantly decrease computational complexity, simplify programming logic, and enhance the automaticity of information systems. Our results demonstrate that the efficiency of the containment test is improved by 30 times, the logic complexity of the intersection area measurement is reduced by a factor of five, and the automaticity of root finding is significantly heightened.
  Keywords: Managing Software Complexity; Information Systems Design; Computing Efficiency; Human Vision System.