Changes in Product Attributes and Costs as Drivers of New Product Diffusion and Substitution

Diffusion theory has typically focused on how communication, internal or external to a social system, leads to adoptions and diffusion of an innovation. We develop a diffusion and substitution model based on a somewhat different perspective. In some cases, progressive improvements in product attributes and/or continual cost reduction seem to be a key driver of the diffusion process. For example, after introduction of the 5.25‐inch disk drive, its capacity continually increased, and accordingly, so did customer willingness‐to‐pay. Our model is based on a linear reservation price framework, in which a product is described by its depth (defined as the difference between a product̂s maximum reservation price and its production cost), and its breadth (related to the slope of its reservation price curve), indicating how broadly it appeals across various customer segments. Because of changes in product depths and breadths over time, customers who previously preferred the old product may later prefer the new product, thus creating the diffusion process. While the Bass model describes diffusion as a function of the coefficients of innovation and imitation, in our model, it is described by the coefficients of depth and breadth (the rates of change in relative depth and breadth), along with an S‐coefficient that we associate with the technology S‐curve. We fit our model to data from the disk‐drive and the microprocessor industries.     

Demand and Production Management with Uniform Guaranteed Lead Time

Recently, innovation‐oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make‐to‐order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead‐time guarantees. We show that the optimal lead time has a closed‐form solution with a newsvendor‐like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation.     

Equilibrium Joining Strategies and Optimal Control of a Make‐to‐Stock Queue

We consider a make‐to‐stock, finite‐capacity production system with setup cost and delay‐sensitive customers. To balance the setup and inventory related costs, the production manager adopts a two‐critical‐number control policy, where the production starts when the number of waiting customers reaches a certain level and shuts down when a certain quantity of inventory has accumulated. Once the production is set up, the unit production time follows an exponential distribution. Potential customers arrive according to a Poisson process. Customers are strategic, i.e., they make decisions on whether to stay for the product or to leave without purchase based on their utility values, which depend on the production manager's control decisions. We formulate the problem as a Stackelberg game between the production manager and the customers, where the former is the game leader. We first derive the equilibrium customer purchasing strategy and system performance. We then formulate the expected cost rate function for the production system and present a search algorithm for obtaining the optimal values of the two control variables. We further analyze the characteristics of the optimal solution numerically and compare them with the situation where the customers are non‐strategic.     

Pricing in a Transportation Station with Strategic Customers

We consider a transportation station, where customers arrive according to a Poisson process, observe the delay information and the fee imposed by the administrator and decide whether to use the facility or not. A transportation facility visits the station according to a renewal process and serves all present customers at each visit. We assume that every customer maximizes her individual expected utility and the administrator is a profit maximizer. We model this situation as a two‐stage game among the customers and the administrator, where customer strategies depend on the level of delay information provided by the administrator. We consider three cases distinguished by the level of delay information: observable (the exact waiting time is announced), unobservable (no information is provided) and partially observable (the number of waiting customers is announced). In each case, we explore how the customer reward for service, the unit waiting cost, and the intervisit time distribution parameters affect the customer behavior and the fee imposed by the administrator. We then compare the three cases and show that the customers almost always prefer to know their exact waiting times whereas the administrator prefers to provide either no information or the exact waiting time depending on system parameters.     

Incentive Schemes for Semiconductor Capacity Allocation: A Game Theoretic Analysis

We study incentive issues that arise in semiconductor capacity planning and allocation. Motivated by our experience at a major U. S. semiconductor manufacturer, we model the capacity‐allocation problem in a game‐theoretic setting as follows: each product manager (PM) is responsible for a certain product line, while privately owning demand information through regular interaction with the customers. Capacity‐allocation is carried out by the corporate headquarters (HQ), which allocates manufacturing capacity to product lines based on demand information reported by the PMs. We show that PMs have an incentive to manipulate demand information to increase their expected allocation, and that a carefully designed coordination mechanism is essential for HQ to implement the optimal allocation. To this end, we design an incentive scheme through bonus payments and participation charges that elicits private demand information from the PMs. We show that the mechanism achieves budget‐balance and voluntary‐participation requirements simultaneously. The results provide important insights into the treatment of misaligned incentives in the context of semiconductor capacity‐allocation.     

An Empirical Study of the Volkswagen Crisis in China: Customers’ Information Processing and Behavioral Intentions

Product‐harm crises usually lead to product recalls, which may cause consumers concern about the product quality and safety. This study systematically examines customers’ immediate responses to the Volkswagen product recall crisis in China. Particular attention was given to customers’ responses to the risk information influencing their behavioral intentions. By combining the protective action decision model and the heuristic‐systematic model, we constructed a hypothetical model to explore this issue. A questionnaire survey was conducted to collect data involving 467 participants drawn from the customers of Volkswagen. We used structural equation modeling to explore the model. The results show that customers’ product knowledge plays an important role in their responses to the crisis. Having more knowledge would make them perceive a lower risk, but they might need even more information, making them more likely to seek and process information, and subsequently increasing their positive behavioral intentions toward the firm (that is pro‐firm behavioral intentions). Risk perception increased customers’ information needs, information seeking, and information processing but decreased their pro‐firm behavioral intentions. In addition to promoting information seeking, information needed to also facilitate customers’ systematic processing and thus increase their behavioral intentions to take corrective action. Customers’ behavioral intentions were also spurred by systematic processing, but failed to be predicted by information seeking. In summary, theoretical and practical implications and suggestions for further research are also discussed.     

Dynamic capacity adjustments with reactive customers

In this paper we develop a behavioural model in which customers come and go based on their perception of waiting time (relative to other facilities) while managers gradually adjust the capacity of the facility based on their perception of demand. We explicitly account for the difference in access to information between existing and potential customers, which implies that the perception of potential customers lags the perception of current customers. We investigate the outcome of the interaction between these simultaneous dynamic decision processes, and in particular the impact of the lags created by the perception formation process and the time to implement desired changes in capacity. These multiple delays may result in customers and service provider being out of step: customers walk away just as the service provider manages to bring extra capacity online.     

Capacity Investment and Product Line Decisions of a Multiproduct Leader and a Focus Strategy Entrant

In this article, I investigate the capacity investment cost conditions where a multiproduct market leader may respond to a focus strategy entrant by using different strategies such as changing the product mix, production volumes, quality levels, and/or by investing in more capacity. The products offered in the market are quality differentiated and customers are heterogeneous in their willingness to pay for quality. The capacity investment costs of the two firms (i.e., the leader and the entrant) may also be different. The classical Stackelberg model predicts that an incumbent does not change its position in response to entry. However, when heterogeneous customer base, product differentiation, and capacity costs are taken into consideration, I find that the leader with a low capacity cost may choose to expand its product line and increase its production. The leader with low capacity cost may introduce a product that it was holding back when the entrant has to bear the high‐capacity cost and cannibalization threat is relatively small. Nevertheless, the extent of production volume strategies reduces as the capacity cost increases for the leader. I also find that when the leader has the power to set the industry standards by deciding the quality levels, as a response to a high‐quality focused entrant, the leader increases both levels of quality and production of the low‐quality product. Moreover, when the capacity investment cost is high for both the entrant and the leader, I find that market prices may increase with entry.     

Pool‐Point Distribution of Zero‐Inventory Products

We study zero‐inventory production‐distribution systems under pool‐point delivery. The zero‐inventory production and distribution paradigm is supported in a variety of industries in which a product cannot be inventoried because of its short shelf life. The advantages of pool‐point (or hub‐and‐spoke) distribution, explored extensively in the literature, include the efficient use of transportation resources and effective day‐to‐day management of operations. The setting of our analysis is as follows: A production facility (plant) with a finite production rate distributes its single product, which cannot be inventoried, to several pool points. Each pool point may require multiple truckloads to satisfy its customers' demand. A third‐party logistics provider then transports the product to individual customers surrounding each pool point. The production rate can be increased up to a certain limit by incurring additional cost. The delivery of the product is done by identical trucks, each having limited capacity and non‐negligible traveling time between the plant and the pool points. Our objective is to coordinate the production and transportation operations so that the total cost of production and distribution is minimized, while respecting the product lifetime and the delivery capacity constraints. This study attempts to develop intuition into zero‐inventory production‐distribution systems under pool‐point delivery by considering several variants of the above setting. These include multiple trucks, a modifiable production rate, and alternative objectives. Using a combination of theoretical analysis and computational experiments, we gain insights into optimizing the total cost of a production‐delivery plan by understanding the trade‐off between production and transportation.     

Optimal Dynamic Order Scheduling under Capacity Constraints Given Demand‐Forecast Evolution

We consider a manufacturer without any frozen periods in production schedules so that it can dynamically update the schedules as the demand forecast evolves over time until the realization of actual demand. The manufacturer has a fixed production capacity in each production period, which impacts the time to start production as well as the production schedules. We develop a dynamic optimization model to analyze the optimal production schedules under capacity constraint and demand‐forecast updating. To model the evolution of demand forecasts, we use both additive and multiplicative versions of the martingale model of forecast evolution. We first derive expressions for the optimal base stock levels for a single‐product model. We find that manufacturers located near their market bases can realize most of their potential profits (i.e., profit made when the capacity is unlimited) by building a very limited amount of capacity. For moderate demand uncertainty, we also show that it is almost impossible for manufacturers to compensate for the increase in supply–demand mismatches resulting from long delivery lead times by increasing capacity, making lead‐time reduction a better alternative than capacity expansion. We then extend the model to a multi‐product case and derive expressions for the optimal production quantities for each product given a shared capacity constraint. Using a two‐product model, we show that the manufacturer should utilize its capacity more in earlier periods when the demand for both products is more positively correlated.     

The Impact of Revenue‐Maximizing Priority Pricing on Customer Delay Costs

Speed is an increasingly important determinant of which suppliers will be given customers' business and is defined as the time between when an order is placed by the customer and when the product is delivered, or as the amount of time customers must wait before they receive their desired service. In either case, the speed a customer experiences can be enhanced by giving priority to that particular customer. Such a prioritization scheme will necessarily reduce the speed experienced by lower‐priority customers, but this can lead to a better outcome when different customers place different values on speed. We model a single resource (e.g., a manufacturer) that processes jobs from customers who have heterogeneous waiting costs. We analyze the price that maximizes priority revenue for the resource owner (i.e., supplier, manufacturer) under different assumptions regarding customer behavior. We discover that a revenue‐maximizing supplier facing self‐interested customers (i.e., those that independently minimize their own expected costs) charges a price that also minimizes the expected total delay costs across all customers and that this outcome does not result when customers coordinate to submit priority orders at a level that seeks to minimize their aggregate costs of priority fees and delays. Thus, the customers are better off collectively (as is the supplier) when the supplier and customers act independently in their own best interests. Finally, as the number of priority classes increases, both the priority revenues and the overall customer delay costs improve, but at a decreasing rate.     

A Strategy for Opening a New Market and Encroaching on the Lower End of the Existing Market

We identify and analyze a scenario where a firm first opens up what we call a “detached” market, by offering a new product that meets a customer need that is very different from (i.e., detached from) the need met by the old established product. Our analysis builds on the previous studies that describe alternate ways in which a new product might open a new market and ultimately encroach on an existing market. Consider the example of cell phones: They opened up a new detached market by meeting the customer need for mobility, a need very different from the traditional attribute of reception quality. By meeting an important detached need, a new product can sell at a high price, even though it might be woefully deficient with regard to the traditional performance dimension (the reception/coverage of early cell phones was sorely lacking). A person who is a high‐end customer for the old product initially despises the new product as a replacement for the old one but might simultaneously be one of the first customers for the new product because it fills the detached‐market need. Over time, the new product improves along the traditional dimension (e.g., cell phone reception/coverage has dramatically improved), and eventually it becomes a replacement for the old product, encroaching from the lower end upward (the first customers to drop their landlines have been lower‐end customers such as students and apartment dwellers, whereas higher‐end business customers still have landlines in their offices). We call this the detached‐market form of low‐end encroachment and show how it helps explain the conundrum of an expensive “disruptive” innovation. We go on to relate our results to the finding that “willingness to cannibalize” is a key factor in an incumbent firm's growth and survival, and to the “blue ocean strategy.”     

A NUMERICAL ANALYSIS OF CAPACITATED POSTPONEMENT

Customer satisfaction can be achieved by providing rapid delivery of a wide variety of products. High levels of product variety require correspondingly high levels of inventory of each item to quickly respond to customer demand. Delayed product differentiation has been identified as a strategy to reduce final product inventories while providing the required customer service levels. However, it is done so at the cost of devoting large production capacities to the differentiation stage. We study the impact of this postponement capacity on the ability to achieve the benefits of delayed product differentiation. We examine a single‐period capacitated inventory model and consider a manufacturing system that produces a single item that is finished into multiple products. After assembly, some amount of the common generic item is completed as non‐postponed products, whereas some of the common item is kept as in‐process inventory, thereby postponing the commitment to a specific product. The non‐postponed finished‐goods inventory is used first to meet demand. Demand in excess of this inventory is met, if possible, through the completion of the common items. Our results indicate that a relatively small amount of postponement capacity is needed to achieve all of the benefits of completely delaying product differentiation for all customer demand. This important result will permit many firms to adopt this delaying strategy who previously thought it to be either technologically impossible or prohibitively expensive to do so.     

Modeling and Managing the Percentage of Satisfied Customers in Hidden and Revealed Waiting Line Systems

We perform an analysis of various queueing systems with an emphasis on estimating a single performance metric. This metric is defined to be the percentage of customers whose actual waiting time was less than their individual waiting time threshold. We label this metric the Percentage of Satisfied Customers (PSC.) This threshold is a reflection of the customers' expectation of a reasonable waiting time in the system given its current state. Cases in which no system state information is available to the customer are referred to as “hidden queues.” For such systems, the waiting time threshold is independent of the length of the waiting line, and it is randomly drawn from a distribution of threshold values for the customer population. The literature generally assumes that such thresholds are exponentially distributed. For these cases, we derive closed form expressions for our performance metric for a variety of possible service time distributions. We also relax this assumption for cases where service times are exponential and derive closed form results for a large class of threshold distributions. We analyze such queues for both single and multi‐server systems. We refer to cases in which customers may observe the length of the line as “revealed” queues.“ We perform a parallel analysis for both single and multi‐server revealed queues. The chief distinction is that for these cases, customers may develop threshold values that are dependent upon the number of customers in the system upon their arrival. The new perspective this paper brings to the modeling of the performance of waiting line systems allows us to rethink and suggest ways to enhance the effectiveness of various managerial options for improving the service quality and customer satisfaction of waiting line systems. We conclude with many useful insights on ways to improve customer satisfaction in waiting line situations that follow directly from our analysis.     

Production and Sales Planning in Capacitated New Product Introductions

How should a firm with limited capacity introduce a new product? Should it introduce the product as soon as possible or delay introduction to build up inventory? How do the product and market characteristics affect the firm's decisions? To answer such questions, we analyze new product introductions under capacity restrictions using a two‐period model with diffusion‐type demand. Combining marketing and operations management decisions in a stylized model, we optimize the production and sales plans of the firm for a single product. We identify four different introduction policies and show that when the holding cost is low and the capacity is low to moderate, a (partial) build‐up policy is indeed optimal if consumers are sensitive to delay. Under such a policy, the firm (partially) delays the introduction of its product and incurs short‐term backlog costs to manage its future demand and total costs more effectively. However, as either the holding cost or the capacity increases, or consumer sensitivity to delay decreases, the build‐up policy starts to lose its appeal, and instead, the firm prefers an immediate product introduction. We extend our analysis by studying the optimal capacity decision of the firm and show that capacity shortages may be intentional.     

Inventory Management for Customers with Alternative Lead Times

It is common for suppliers operating in batch‐production mode to deal with patient and impatient customers. This paper considers inventory models in which a supplier provides alternative lead times to its customers: a short or a long lead time. Orders from patient customers can be taken by the supplier and included in the next production cycle, while orders from impatient customers have to be satisfied from the on‐hand inventory. We denote the action to commit one unit of on‐hand inventory to patient or impatient customers as the inventory‐commitment decision, and the initial inventory stocking as the inventory‐replenishment decision. We first characterize the optimal inventory‐commitment policy as a threshold type, and then prove that the optimal inventory‐replenishment policy is a base‐stock type. Then, we extend our analysis to models to consider cases of a multi‐cycle setting, a supply‐capacity constraint, and the on‐line charged inventory‐holding cost. We also evaluate and compare the performances of the optimal inventory‐commitment policy and the inventory‐rationing policy. Finally, to further investigate the benefits and pitfalls of introducing an alternative lead‐time choice, we use the customer‐choice model to study the demand gains and losses, known as demand‐induction and demand‐cannibalization effects, respectively.     

Service Systems with Experience‐Based Anecdotal Reasoning Customers

The existing queueing literature typically assumes that customers either perfectly know the expected waiting time or are able to form rational expectations about it. In contrast, in this article, we study canonical service models where customers do not have such full information or capability. We assume that customers lack full capability or ample opportunities to perfectly infer the service rate or estimate the expected waiting time, and thus can only rely on past experiences and anecdotal reasoning to make their joining decisions. We fully characterize the steady‐state equilibrium in this service system. Compared with the fully rational benchmark, we find that customers with anecdotal reasoning are less price‐sensitive. Consequently, with a higher market potential (higher arrival rate), a revenue‐maximizing firm may increase the price if the service rate is exogenous, and it may decrease the price if the service rate is at the firm's discretion. Both results go against the commonly accepted pricing recommendations in the fully rational benchmark. We also show that revenue maximization and welfare maximization lead to fundamentally different pricing strategies with anecdotal reasoning, whereas they are equivalent in the fully rational benchmark.     

Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

The subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry. The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.     

Product and service innovation with customer recognition

Product and service innovation is important for brands to succeed in a competitive marketplace. As information technology advances, customer recognition becomes a growing industry trend; that is, brands track customers' purchase history, recognize and price discriminate between repeat and new customers.The trend of customer recognition has changed the nature and intensity of competition between brands. In this article, we examine how customer recognition and the associated changes in competition affect brands' incentives to invest in product and service innovation. We find that when brands have similar equity, customer recognition increases brands' incentives to invest in product and service innovation. However, when brands have sufficiently different equity, customer recognition leads the stronger brand to invest more and the weaker brand to invest less in product and service innovation. In addition, extant literature suggests that customer recognition reduces brand profits. In contrast, we find that customer recognition can increase the weaker brand's profit but decreases it more for the stronger brand. Thus, collecting customers' purchase history data for customer recognition can be beneficial for weaker brands but detrimental for stronger brands.     

Collaborative Innovation with Customers: A Review of the Literature and Suggestions for Future Research*

Collaborative innovation with customers or users is increasingly important for the development of new products and services. In this paper we provide a review of the literature, placing emphasis on how firms engage in collaborative innovation with individual and business customers. Our review develops a synthesized conceptual framework from three existing models, and other streams of research, to organize this diverse body of literature. We review studies from several disciplines including innovation, strategy, management, marketing and information technology. Although much of the literature assumes net positive benefits from such collaboration, we also discuss contrary perspectives. We highlight areas in which research is needed for greater understanding of the strategic issues and for managing the collaborative process, and provide suggestions for future studies.