Knowledge management in banking: A bibliometric literature review

Risk management is one of the prominent issues which are pivotal to the success of a business and may adversely affect profitability if not properly practised. Therefore, the main objective of this paper was to determine risk management practices in manufacturing SMEs in Cape Town. The research conducted was quantitative in nature and constituted the collection of data from 74 SME leaders, all of whom had to adhere to a list of strict delineation criteria. All data collected were thoroughly analyzed through means of descriptive statistics. From the findings made, it is clear that SMEs in the manufacturing sector do in fact understand risk management initiatives applicable to ‘manage’ their respective businesses towards sustainability, but not to a large extent. It was found that respondents are unaware of the elements which make risk management effective, which ultimately aids to the development of problems for SMEs. All employees, managers and owners must coordinate their efforts together to identify and manage organizational risks within their ambit to obtain total risk coverage, as well as provide assurance that these risks are effectively managed from a coordinated approach. Further studies may be carried out to identify measures that can be taken to improve the effectiveness of risk management practices in SMEs.

Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities.

This paper aims to discuss the concepts and methodological issues of enterprise risk management (ERM). The case study of company A shows that ERM has been implemented and integrated with management control as a means of monitoring its subsidiaries. First, ERM system was implemented through comprehensive review of corporate risk policies, risk management processes, roles and responsibilities, and risk culture. Second, company A integrated ERM with the existing management control system in order to evaluate the risk underlying the current management activities. Finally, ERM implementation was expanded to all subsidiaries so that each business unit would be delegated for its own risk management. This paper provides insight on the process how group-level internal auditors can use ERM as a tool to manage risk of subsidiaries, thereby filling the gap between academic research and practice. This successful ERM adoption case can be used as a guideline for other organizations, which plan to adopt ERM with reduced costs and improved processes.