The state of implementing big data in banking business processes: An Indonesian perspective

Knowledge Management (KM) implementation is as much a challenge as it is a great opportunity for businesses worldwide in several sectors. The controversy over its worthwhile application persists among practitioners and scholars, with claims having arisen that business opportunities and performance are enhanced through KM adoption. Conversely, challenges regarding effective implementation of KM concerning individuals’ behaviors, beliefs, and practices prompt significant questioning regarding its complex basis: individual-technology-process-production. The purpose of this work is to shed light on some perspectives for the effective adoption of knowledge management technology, the consolidation of a favorable and aligned organizational culture, and patterns of use and retrieval of information in organizations. This study brings into focus the concept of knowledge management and technology, and culture in the organization through a systematic literature review and based on the researcher’s hypothesis and observations that successful KM implementation results from a clear understanding of its multidisciplinary, complex, and multifaceted process, which requires a methodical and strategic business practice concerning the application of technological tools, the establishment of a learning culture through shared vision of business’ and individuals’ needs, and consequential enhancement of processes and solutions. It concludes that investment in KM practice and the consolidation of a learning culture are correlated, reflecting a type of leadership that is favorable to the flow of information, generation, and sharing of knowledge (the intellectual capital) and optimization of decision making and business performance.

Several systems of budgeting have been introduced into practice over the last fifty years. While more popular budgeting systems such as incrementalism and Planning Programming Budgeting Systems (PPBS) have seen continued popularity and resilience as budgeting systems, zero-based budgeting (ZBB) has experienced a resurgence in popularity, as governments and public organizations alike seek to control wasteful spending within their departments. The aim of this article is to provide users with a step-by-step guide to designing zero-based budgeting for public organizations. This article begins by describing the foundations of zero-based budgeting, as well as providing a brief comparative exploration of ZBB alongside other contemporary budgeting systems. To gain a deeper understanding of zero-base budgeting and the potential, as well as the drawbacks that this type of budgeting system holds, this paper examines the actual experience of several organizations that have implemented the zero-based budgeting method. This is accomplished by conducting a critical review of all the research and case studies that have been conducted on this topic. It is the hope of the author that both public sector and non-profit organizations will benefit from the findings of this paper when considering the implementation of their own zero-based budgeting, resulting in better planning and performance evaluation.

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.