Consumers’ perceptions of visual merchandising can arouse consumers’ in-store merchandise exploration, such as interacting with products, differentiate a retail brand among competitors; contribute to brand preference; and encourage purchase intentions. However, the combination of topics, visual merchandising and cognition, remains relatively unexplored. Thus, Study I develops measures of visual merchandising cognition and Study II examines the impact of visual merchandising cognition on brand preference, with a focus on fashion brands. This research used mixed methods and an experimental design to empirically test the influence of visual merchandising cognition on fashion brand preferences. Confirmatory factor analysis finds three dimensions of visual merchandising cognition: in-fashion, attractiveness, and function. A structural equation model confirms a conceptual framework for the influence of visual merchandising cognition on brand preferences. In-fashion and attractiveness have a significantly positi...

How does visual merchandising in fashion retail stores affect consumers’ brand attitude and purchase intention?

Exponential technological expansion creates opportunities for competitive advantage by applying new data-oriented approaches to digital marketing practices. Machine learning (ML) can predict future developments and support decision-making by extracting insights from large amounts of generated data. This functionality greatly impacts and streamlines the strategic decision-making process of organizations. The research gap analysis revealed that a little is known about marketers’ attitude toward, and knowledge about, ML tools and their adoption and utilization to support strategic and operational management. The research presented here focuses on the selection and adoption of the ML-driven analytical tools by three distinct groups: marketing agencies, media companies, and advertisers. Qualitative and quantitative research was conducted on a sample of these organizations operating in Slovakia. The findings highlight: 1) the important role of intelligent analytical tools in the creation and deployment of marketing strategies; 2) the lack of knowledge about emerging technologies, such as ML and artificial intelligence (AI); 3) the potential application of the ML tools in marketing, and; 4) the low level of adoption and utilization of the ML-driven analytical tools in marketing management. A framework consisting of enablers and a process map was developed to help organizations identify the opportunities and successfully execute projects that are oriented toward the deployment and adoption of the analytical ML tools in digital marketing.

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Towards the Adoption of Machine Learning-Based Analytical Tools in Digital Marketing

The aim of this paper is to provide an overview on how artificial intelligence is shaping the digital era, in policy making and governmental terms. In doing so, it discloses new opportunities and discusses its implications to be considered by policy-makers. The research uses a systematic literature review, which includes more than one technique of data analysis in order to generate comprehensiveness and rich knowledge, we use: a bibliometric analysis and a content analysis. While artificial intelligence is identified as an extension of digital transformation, the results suggest the need to deepen scientific research in the fields of public administration, governmental law and business economics, areas where digital transformation still stands out from artificial intelligence. Although bringing together public and private sectors, to collaborate in the public service delivery, presents major advantages to policy makers, evidence has also shown the existence of negative effects of such collaboration.

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Artificial Intelligence in Government Services: A Systematic Literature Review

This study examines consumers’ perceptions of retail store brands and identifies the predictors of purchase intentions for the store brands. To examine the proposed research model, two independent samples are drawn. The first sample consists of 120 responses collected via mall intercept at a famous hypermarket retail chain store, and the second sample consists of 120 responses also collected using the mall intercept method at a supermarket chain store in Klang Valley, Malaysia. Multiple regressions are used to test the hypotheses. There are mixed results in relation to the tested relationships. Perceived value for the money, perceived quality variance, perceived price and perceived risk have a significant impact on consumer purchase intention for the store brand in the hypermarket sample. However, analysis using the supermarket sample indicated that only perceived quality variance has a significant impact on consumer purchase intention for the store brand.Keywords: Consumer Perception, Purchase Intention, Store BrandJEL Classification: M31

Perceptions and Predictors of Consumers’ Purchase Intentions for Store Brands: Evidence from Malaysia

Why doesn't our value creation payoff: Unpacking customer analytics-driven value creation capability to sustain competitive advantage

In the current business environment, more uncertain than ever before, understanding consumer behavior is an integral part of an organization's strategic planning and execution process. It is the key driver for becoming a market leader. Therefore, it is important that all processes in business are customer centric. Marketers need to harness big data by engaging in data driven-marketing (DDM) to help organizations choose the “right” customers, to “keep” and “grow” them and to sustain “growth” and “profitability”. This research examines DDM adoption practices and how companies can aim to enhance shareholder value by bringing about “customer centricity”.,An online survey conducted in 2016 received 180 responses from junior, middle and senior executives. Of the total responses, 26% were from senior management, 39% from middle management and the remaining 35% from junior management. Industries represented in the survey included retail, BFSI, healthcare and government, automobile, telecommunication, transport and logistics and IT. Other industries represented were aviation, marketing research and consulting, hospitality, advertising and media and human resource.,Success of DDM depends upon how well an organization embraces the practice. The first and foremost indicator of an organization's commitment is the extent of resources invested for DDM. Respondents were divided into four categories; Laggards, Dabblers, Contenders and Leaders based on their “current level of investments” and “willingness to enhance investments” soon.,With storming digital age and the development of analytics, the process of decision-making has gained significant importance. Judgment and intuition too are critical to the process. Choosing an appropriate action cannot be done strictly on a rational basis.,The results of the study offer interesting implications for managing the growing sea of data. An iterative and incremental approach is the need of the hour, even if it has to start with baby steps, to invest in and reap the fruits of DDM. The intention to use any system is always dependent on two primary belief factors: perceived usefulness and perceived ease of use; however, attitudes and social factors are equally important.,There is a dearth of knowledge with regards to who is and is not adopting DDM, and how best big data can be harnessed for enhancing effectiveness and efficiency of marketing budget. It is, therefore, imperative to build a knowledge base on DDM practices, challenges and opportunities. Better use of data can help companies enhance shareholder value by bringing about “customer centricity”.

Data-driven marketing for growth and profitability

Purpose – A retail brand is an asset of value to the stakeholders Nurturing it enhances quality and stability of earnings Franchising it across emerging countries provides an opportunity for growth The challenge, however, is to ensure that the brand stays “connected” with the local markets and “relevant” during changing times Current research is highly inadequate in guiding the retail brands to stay vibrant when traveling to emerging markets This research aims to look at how a successful fashion brand in the UK is struggling to grow its retail business through franchise in the Middle East While the opportunity is vast, the retail brand has been struggling for it has not stayed “relevant” The paper seeks to present a framework for monitoring its performance so the retail brand can “stay alive”Design/methodology/approach – Qualitative research was done to understand the profile of the walk‐in customers, their shopping behavior, spending motives, and lifestyles Quantitative research was done to asce

Nitin Patwa

Papers

Data-driven marketing for growth and profitability

Navigating retail brands for staying alive