Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach

In an environment with digital disruptions, retailers must adopt a customer-centric approach to survive and compete effectively. Retailers need to be agile and forward-looking in adopting the relevant analytics and performance metrics to bring a customer-centric approach across upstream and downstream activities in the retail value chain. However, retailers in emerging markets (EMs) need clarity on the specific analytics and performance metrics in the value chain that will enable them to transition from their current product-centric state to the desired customer-centric state. Employing a triangulation approach (i.e., literature review, marketplace evidence, and managerial interviews) in the fragmented retail landscape of EMs, this study provides an organizing framework that explains: (i) the need for a customer-centric approach across the retail value chain, (ii) the specific performance metrics that need to be adopted across upstream and downstream activities in the retail value chain to enable EM retailers to achieve their desired customer-centric state, and (iii) the role of analytics in providing insights to achieve these performance metrics and improving monetary and non-monetary firm performance outcomes. We also provide firm-specific and macro-level conditions that can influence the EM retailers’ adoption of relevant analytics and explain the different paths retail formats can follow to adopt analytics. We present a strategy matrix that enables retail managers to identify the appropriate analytics to be adopted at different retail value chain stages to achieve desired performance metrics. We also highlight future research opportunities in retailing in EMs.     

Correcting analytics maturity myopia

Companies face increasing pressure to compete in the practice of analytics and strive for analytics maturity to sustain their competitive advantage. A single-minded, narrow focus on gaining analytics maturity, however, leads to analytics maturity myopia. Based on our studies of analytical capabilities and numerous conversations with executives and managers, we offer a scorecard for organizations to identify the presence of analytics maturity myopia and propose a framework for organizations to correct this issue. The proposed framework partially explains the mixed and conflicting results regarding the relationship between analytics maturity and business value found in the literature. Specifically, we recommend that companies focus on three factors that are critical to realizing value from analytics initiatives: (1) a balanced view of value to different stakeholders, (2) a continuous expansion of the business ecosystem beyond current stakeholders to identify and pursue new opportunities, and (3) use of an emergent strategy to take advantage of unexpected opportunities and develop organizational agility.     

Managerial work in the realm of the digital universe: The role of the data triad

With the explosion of the digital universe, it is becoming increasingly important to understand how organizational decision making (i.e., the business-oriented perspective) is intertwined with an understanding of enterprise data assets (i.e., the data-oriented perspective). This article first compares the business- and data-oriented perspectives to describe how the two views mesh with each other. It then presents three elements in the data-oriented perspective that are collectively referred to as the data triad: (1) use, (2) design and storage, and (3) processes and people. In describing the data triad, this article highlights practices, architectural techniques, and example tools that are used to manage, access, analyze, and deliver data. By presenting different elements of the data-oriented perspective, this article broadly and concretely describes the data triad and how it can play a role in the redefined scope of work for data-driven business managers.     

组织视阈下大数据分析能力的整合分析框架构建与未来展望

面对数字化情境，企业通过多元化开发与策略性利用大数据分析能力获得高绩效和竞争优势。大数据分析能力可用于解释企业如何依托数据发挥经济效应，也可为解释企业差异化绩效提供新证据。通过阐述大数据分析能力的概念内涵与测量方法，分析其前置动因和作用机制，构建大数据分析能力整合分析模型，并提出未来潜在研究方向：从资源、能力和整合3种视角丰富大数据分析能力的概念内涵和测量工具；结合社会整合理论，挖掘大数据分析能力的差异化作用机制；从组织支持和团队领导两方面探索大数据分析能力的形成机制；关注大数据分析能力发生的重要情境和边界条件。结论有助于全面认识大数据分析能力的形成过程和作用机制，丰富其在组织管理领域的研究。