This paper describes a general approach for dynamic control of stochastic networks based on fluid model analysis, where in broad terms, the stochastic network is approximated by its fluid analog, an associated fluid control problem is solved and, finally, a scheduling rule for the original system is defined by itnerpreting the fluid control policy.
This paper describes a family of discrete-review policies for scheduling open multiclass queueing networks. Each of the policies in the family is derived from what we call a dynamic reward function: such a function associates with each queue length vector q and each job class k a positive value rk(q), which is treated as a reward rate for time devoted to processing class k jobs. Assuming that each station has a traffic intensity parameter less than one, all policies in the family considered are shown to be stable.
Generalized knowledge comes from cumulating results across studies, a process known as meta-analysis. Efficiently increasing generalized knowledge in a defined area-estimates of price or advertising, for example-is one important goal for research. Because (1) most meta-analyses are based on highly inefficient and unbalanced natural experiments or designs and (2) additional studies are costly, carefully selecting the next study is important.
Why has the service factory model failed to live up to its original promise? To answer this question, we start with a basic concept: service is doing the work of your customer. As a result, it requires a high level of contact, communication and coordination with your customers. To deliver truly excellent service, therefore, requires a level of customer intimacy. That is, a service provider needs to know individual customers being served in order to deliver service that, in addition to being efficient, is also personal and effective in fulfilling their total service requirements.
This paper offers an exact definition of the value created by firms together with their suppliers and buyers. The "added value" of a firm is similarly defined, and shown under certain conditions to impose an upper bound on how much value the firm can capture. The key to a firm's achieving a positive added value is the existence of asymmetries between the firm and other firms. The paper identifies four routes ("value-based" strategies) that lead to tthe creation of such asymmetries. Our analysis reveals the equal importance of a firm's supplier and buyer relations.
This study examines how advertising budget setting, framed as a prisoner's dilemma, is affected by information on the competitive situation and characteristics of the decision maker. Hypotheses are tested using experiments in which subjects set advertising budgets. Results indicate that subjects were generally competitive, but also based their strategy selections on what they expected their opponents to do, what their opponents did last time, whether the competitive relationship was expected to continue, market shares, and whether the subject's profit objectives were short- or long-term.
A conceptual model is developed that describes the relationships among consumer values, utility, and ownership of durables. These relationships are tested empirically using data on a variety of discretionary durables collected from a sample of 735 adults. Results support the model structure and suggest that augmenting the List of Values (Kahle 1983) with a measure of materialism improves prediction of value-related consumer behavior.
An idea is a collection of existing concepts or words. What makes an idea original or appealing is how these concepts or words are combined in the context in which they appear. Similarly, a food recipe is a combination of ingredients, and it is often evaluated based on how these ingredients fit together to form the whole. In this research, we leverage representation learning methods, specifically word embeddings, to measure the fit among ingredients in the recipe and capture the possibly complex interactions between these ingredients.
Generative AI’s recent advancements in creating content have offered vast potential to transform the advertising industry. This research investigates the impact of generative AI-enabled visual ad creation on real-world advertising effectiveness. For this purpose, we collaborate with a display ad platform and leverage a quasi-experimental setting that includes over two million ad-day observations by over seven thousand advertisers across nearly 50 product categories, encompassing more than 16 billion ad impressions and 116 million clicks.