The center focuses on the use of consumer behavioral models, business analytics methods, Web 4.0 technologies, and information technologies to analyze existing business/e-business models, consumer behaviors, retail management, market trends, and new market or product opportunities. The research methods employed include: qualitative analysis, quantitative analysis, social media analysis and big data analysis. The center works with its partners to develop new business models, strategic plans, and innovative applications for local and international market so based on researched consumer behaviors, customer knowledge data, market trend analysis, and lessons learnt from partner business cases.
To identify the potential local or international market opportunities, big data analytics provides a novel method to capture market data and integrate findings into the business data for analysis. Import and export trade analysis, product analysis, market trend analysis, consumer shopping behavioral analysis, competitor analysis, supplier analysis, cultural analysis, business infrastructure analysis, government policy, rule and regulation analysis and tariff analysis require data from various sources and the data formats are different. To relate and integrate these data for analysis, statistics, data mining, text mining, web mining, social media and business data taxonomies are used and along with a qualitative data analysis framework to derive the analysis of market opportunities.
Traditional e-business strategies focus on business model development, market globalization, consumer shopping behaviors, e-payment methods, security and privacy issues, e-customer relationship management and e-business application development. As social media channels continue to grow, social media will become the future channel for business, including: product advertisement, transaction processing, product review, customer relationship management and market analysis. The invention of fintech, semantic web and blockchain technologies set up the distributed based information architecture for data flow, information exchange, storage and knowledge discovery. These technologies allow Person to Person (P2P) social marketing and transaction execution in the new block chain environment. P2P transaction data will be used by social media virtual agents for analyzing, monitoring and tracking the P2P customers' behaviors in social media networks for future personalized product recommendations, cross-selling and up-selling.
Understanding consumer behaviors including personality, product navigation and selection processes, psychological changes based on the market environment, peer or community influences, and purchase intention are some of the critical success factors for market analysis and campaign development. Beyond the traditional survey approach, new research has shown that new methods like social media monitoring and tracking, community networking and interaction analysis, customer referral cycle, and blockchain analysis can be used to capture customers' internet surfing and purchasing behaviors.
Business case studies provide the real-world lessons of various business problems and solutions. Scenario analysis, problem analysis, storytelling, metadata analysis, content analysis, information coding and grounded theory are the common qualitative methods used for case analysis. The business's critical success factors and failure factors are analyzed, documented and summarized in the case study analysis. Novel methods used for case analysis include: knowledge management techniques, process mapping analysis, narrative analysis, semantic network, knowledge mapping, business ontology and intellectual capital measurement to fully explore the business case. This analysis allows for students to learn best practices in business management and quality improvement. Business case studies are maintained in a repository for learners to build their business knowledge.