Indian textile suppliers' sustainability evaluation using the grey approach

Textile supply chains consist of multinational garment retailers (customers), garment manufacturers (suppliers), and ancillary suppliers (suppliers to manufacturers). This paper evaluates suppliers' within the Indian textile and clothing industry (both garment manufacturers and ancillary suppliers) using sustainability criteria. Examining a sample of sixty-three suppliers and six sustainability criteria (i.e. discrimination, abuse of human right, child labor, long working hours, unfair competition, and pollution), we categorize suppliers into the three categories of ‘good performer’, moderate performer’, and ‘performance not up to expectation’. Since all the criteria are potentially subjective, we have employed the grey approach for analysis. The results indicate that the criterion of long working hours is a critical one for both categories of suppliers; in the case of garment manufacturers, we found that pollution and unfair competition were the most important criteria. In addition, employing child labor was found to be a critical criterion in the case of ancillary suppliers.     

If Time Is Money Is It a Common Currency? Time in Anglo,Asian, and Latin Cultures

ABSTRACT

Social time affects the timing and frequency of purchases, the hours consumers patronize businesses, and how long consumers expect products to last. Understanding how culturally based social time attitudes differ will help marketers tailor their offerings and messages to consumers whose concepts of time differ. This exploratory study combined related streams of time literature to examine constructs of social time differences across cultures. Drawing upon a sample of 1377 respondents from six countries representing three cultural groups (Anglo, Latin, and Asian), the study explores the similarity and differences of cultural social time differences among these three groups.     

CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions

Many retailers have collected large amounts of customer data using, for example, loyalty programs. We provide an overview of the extant literature on customer relationship management (CRM), with a specific focus on retailing. We discuss how retailers can gather customer data and how they can analyze these data to gain useful customer insights. We provide an overview of the methods predicting customer responses and behavior over time. We also discuss the existing knowledge on the application of marketing actions in a CRM context, while providing an in-depth discussion on CRM and firm value. We outline future research directions based on the literature review and retail practice insights.     

Will the frog change into a prince? Predicting future customer profitability

More and more companies have customer databases that enable them to analyze customer profitability over time. These companies often seek to determine the most important customers as indicated by their current or historical profitability and focus attention on them. Focusing on profitable customers can result in more efficient use of marketing resources, but this approach neglects the fact that customers can evolve over time. Some customers begin as low-profit customers but eventually develop into high-profit customers. Others may start out as high-profit customers but become unprofitable over time. Previous efforts to predict future profitability have been relatively unsuccessful, with relatively simple, naïve models often performing just as well as or better than more sophisticated ones. Our paper presents a new approach to predicting customer profitability in future periods that performs significantly better than naïve models. We estimate the models on data from a high-tech company in a business-to-business context and validate the models' predictive ability on a holdout sample.We show that a model based on simulation of customer futures provides large improvements over naïve extrapolation of average profits. By using the simulation model to select customers, ROI from marketing efforts is projected to increase by 58%.     

The interrelationships between brand and channel choice

We propose a framework for the joint study of the consumer’s decision of where to buy and what to buy. The framework is rooted in utility theory where the utility is for a particular channel/brand combination. The framework contains firm actions, the consumer search process, the choice process, and consumer learning. We develop research questions within each of these areas. We then discuss methodological issues pertaining to the use of experimentation and econometrics. Our framework suggests that brand and channel choices are closely intertwined, and therefore studying them jointly will reveal a deeper understanding of consumer decision making in the modern marketing environment.     

Improving customer profit predictions with customer mindset metrics through multiple overimputation

Journal of the Academy of Marketing Science - Research and practice have called for the incorporation of customer mindset metrics (CMMs) to improve the accuracy of models that predict individual...     

Knowing what to sell, when, and to whom

Despite an abundance of data, most companies do a poor job of predicting the behavior of their customers. In fact, the authors' research suggests that even companies that take the greatest trouble over their predictions about whether a particular customer will buy a particular product are correct only around 55% of the time--a result that hardly justifies the costs of having a CRM system in the first place. Businesses usually conclude from studies like this that it's impossible to use the past to predict the future, so they revert to the timeworn marketing practice of inundating their customers with offers. But as the authors explain, the reason for the poor predictions is not any basic limitation of CRM systems or the predictive power of past behavior, but rather of the mathematical methods that companies use to interpret the data. The authors have developed a new way of predicting customer behavior, based on the work of the Nobel Prize-winning economist Daniel McFadden, that delivers vastly improved results. Indeed, the methodology increases the odds of successfully predicting a specific purchase by a specific customer at a specific time to about 85%, a number that will have a major impact on any company's marketing ROI. What's more, using this methodology, companies can increase revenues while reducing their frequency of customer contact-evidence that overcommunication with customers may actually damage a company's sales.     

The Role of Economics: Creating the Sustainable Rational Agent

How are consumption decisions made? Does demand drive supply, does supply drive demand, or is it a mixture of both? Do consumption decisions implicitly include our values or are our decisions made with an assumption that what we are consuming has already factored in the long-term best interests of human health and environmental well-being? These basic questions are often unaddressed in the introductory study of economics. Instead, assumptions related to consumption and production behavior, demand, and supply are embedded as representative of consumer and producer behavioral norms, typically with minimal discussion. Could implicit assumptions promote observable behavior rather than be indicative of it? If so, could increasing awareness of the role of embedded values in demand and supply influence behaviors of consumers and producers and thereby alter economic outcomes? These questions were addressed as part of an introductory economics course case study. The purpose of the study was to raise awareness of the implications of individual purchasing decisions and to specifically initiate students in self-evaluation of their individual values and the values embedded in their individual consumption behavior. The results of the study promote the view that increasing awareness of the holistic impact of consumption behavior may be a significant catalyst to promoting sustainability.     

Do market characteristics impact the relationship between retailer characteristics and online prices?

We propose that market characteristics interact with retailer characteristics to determine online prices. The retailer characteristics examined include—service quality of a retailer, channels of transaction provided by a retailer and the size of a retailer. The market characteristics capture the level and nature of competition, and the price level of a product. We utilize a Hierarchical Linear Model (HLM) framework for capturing and testing the proposed interactions. The better fit between the model and the online market structure is reflected by a twenty-five percent increase in explainable price dispersion over results from comparable studies. Our study demonstrates that while retailer characteristics do impact online prices, this influence is significantly enhanced or diminished by the accompanying market characteristics.