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A discussion of call center agent work selection preferences is presented in the context of what is referred to herein as the accept–avoid decision. In call centers where agents are given autonomy to select calls from a shared queue for work without a standard routing mechanism, it is likely that each agent uses a different set of criteria and has different preferences which influence their decision to accept a call or avoid it. In order to understand such preferences, simple heuristics are developed and implemented into an additive linear model as an example to estimate the derived utility an agent may receive from the decision to accept a call. The game theoretic implications of such decision making by a group or team of agents are also discussed to illustrate the dilemma of the universally avoided call, and how agents may compete for acceptance or avoidance of particular calls.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Quality Function Deployment (QFD) proposes to take into account the “voice of the customer,” through a list of customer needs, which are (qualitatively) mapped to technical requirements in House One. But customers do not perceive products in this space, nor do they not make purchase decisions in this space. Marketing specialists use statistical models to map between a simpler space of customer perceptions and the long and detailed list of needs. For automobiles, for example, the main axes in perceptual space might be categories such as luxury, performance, sport, and utility. A product’s position on these few axes determines the detailed customer requirements consistent with the automobiles’ position such as interior volume, gauges and accessories, seating type, fuel economy, door height, horsepower, interior noise level, seating capacity, paint colors, trim, and so forth. Statistical models such as factor analysis and principal components analysis are used to describe the mapping between these spaces, which we call House Zero.

This paper focus on House One. Two important steps of the product development process using House One are: (1) setting technical targets; (2) identifying the inherent tradeoffs in a design including a position of merit. Utility functions are used to determine feature preferences for a product. Conjoint analysis is used to capture the product preference and potential market share. Linear interpolation and the slope point formula are used to determine other points of customer needs. This research draws from the formal mapping concepts developed by Nam Suh and the qualitative maps of quality function deployment, to present unified information and mapping paradigm for concurrent product/process design. This approach is the virtual integrated design method that is tested upon data from a business design problem.

Irrespective of the popularity of country-of-origin research in international marketing, no attention has been paid to effects which stem from the declaration of a product's local origin, like “Made in City X”. In this study, insights from country-of-origin research as well as exploratory qualitative studies are used to model determinants of preference for local products. Conjoint analysis results based on a sample of consumers from three neighboring cities in Germany show the importance of local origin for product preference. Structural equation analysis sheds first light on the mechanism of city-of-origin effects.

This paper focuses on customer value analysis and measurement, framing customer value management as one of the main antecedents of the company value-creation process. The paper builds on three main pillars. First, the paper highlights the critical role of customer value in business-to-business markets, focusing on the links between the company's ability to manage customer value-creation processes and the positive financial and economic outcomes generated by loyalty effects. Secondly, the paper develops key analytical stages for an understanding of customer value. The focus is on the customer value-chain concept, including consideration of the customer information and acquisition process and its decision rules. Third, the paper illustrates the measurement process, offering an organizational framework for selecting the most suitable method for measuring perceived customer value. The methodological alternatives range from desk measures (e.g., technical computation of the total cost of ownership (TCO)) to field analysis, like those considered under both compositional and the decomposition approaches (e.g., conjoint analysis). The paper concludes with remarks on the managerial implications of these measures, as well as offering suggestions for further research on value for the customer.

Most aspects of brand loyalty rely heavily on brand awareness. However, the information processing of brand awareness in customers, which is significant in buying decisions or customers' feedback experiences, is still unknown in the available literature. This study investigates how dynamic brands awareness affect customers in buying what they buy and how they buy them. The current study's entire goal is to comprehend the phenomenon of customer preferences and choices in the Nigerian context. The study asserts that there is a need to provide critical information that will improve customers' hypotheses testing in making a choice and preferences in creating brand awareness. It is necessary for brand builders and marketers to achieve and maintain the ultimate level of awareness through great gift items, pricing tactics, sales promotion, and sponsorship of programmes and events creatively and consistently. Meaningful customer relationship management programmes and activities must be established to constantly improve brand awareness.

In this research, the effect of green logistics in e-commerce on consumer purchasing behaviour is examined. Companies that have adopted green logistics are thought to be effective on consumer’s product preferences. Businesses are sensitive to changing environmental conditions and changing consumer behaviour and that they adopt green logistics in line with their development goals is a factor that can affect the purchasing behaviour of consumers. Therefore, ‘adoption of the green principle’ becomes a social and global phenomenon.

When consumers evaluate a product or service, they look at not only product performance and quality, but also the environmental impact of the product. The fact that the enterprises realised this, their responsibilities in terms of environmental impact, their achieving competitive advantage, and their aim to strengthen their brand image in the market led them to adopt the green principle. In this respect, the importance of R&D studies has been understood and activities related to this subject have increased. These activities ultimately reduced both living conditions and lifecycle costs. Green brand awareness has increased in consumers. The use of technology in commerce has expanded e-commerce and has become a part of life. In this context, it should be investigated whether the development of environmental consciousness of the consumer in e-commerce has an effect on the product selection. According to the findings of the research about the effect of green logistics practices on the buying behaviour of firms, it is determined that the environmental awareness and demands of the consumers are highly effective in the adoption of green logistics and that the green logistics practices in e-commerce affect the purchasing behaviour of the consumers. At this point, a green approach in national or international commercial activities has been exhibited and also needs to be executed.

Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.