In B2B markets, when firms sign contracts for transactions pertaining to the exchange of services that are delivered over a period of time, one critical decision they make is the length (or duration) ...of the contract. If the services are hired for a long project, companies often sign multiple, successively run contracts with the same vendor. This is prevalent in projects such as when multinational companies hire consulting firms like Accenture to streamline and digitize their business processes, when big banks in developing countries hire firms like Tata Consultancy Services to extend banking facilities into rural markets, and when oil companies hire rig firms to drill oil wells. From a traditional economic perspective, companies would decide on an optimal contract length that is not too long or too short; the former disables the firms from reacting to market changes while the latter makes negotiation costs expensive. However, when a company signs a series of successive contracts with a service-firm, both companies get to learn about the other company’s goals and operations dynamically, which might influence the length of each contract in the series. Thus, determining the contract length in a series of successive contracts is more challenging. In this study, we build a contract length determination model that considers both the economic factors and the dynamic learning. The model provides managers with a theoretical yet practical tool to make optimal decisions on contract length. We use data from the oil-drilling industry to empirically test the proposed model.
Purpose
Companies in the B2B service sector often sign a series of successive contracts instead of one long contract with their vendors. Economic researchers have shown how the lengths of stand-alone ...contracts are influenced by economic factors such as asset specificity and economic volatility, but have not researched into contracts that are part of a continuous series. The purpose of this study was to explore if being a part of a series of contracts influences the length of the focal contract and the rental rate.
Design/methodology/approach
The authors use data collected from the oil drilling industry to empirically test their hypotheses. The data set consists of 2,621 contracts involving jack-up rig hiring in the Gulf of Mexico region.
Findings
The authors empirically show that the series duration affects both the length and rental rate of each constituent contract, even after considering all other plausible economic factors. Specifically, the duration of a series has a positive effect on the length and a negative effect on the rental rate of the constituent contract.
Originality/value
Although contract length is as vital as the rent in B2B service transactions, it is rather unfortunate that marketing scholars have not researched much into this topic. The findings offer a new insight into the forces that shape the B2B service contracts and thus help the B2B managers make a better decision in service contracts.
We investigate consumer preference for online versus offline purchasing of a complex service (home mortgage), across the three stages of purchasing, namely, pre-purchase, purchase, and post-purchase. ...Our analysis of data from 300 consumers shows that (1) the offline channel is generally preferred over the online channel across all the stages, and (2) the channel usage intention in a particular stage is moderated by the consumer's Internet experience. Specifically, in both the pre- and post-purchase stages, the usage intention for the online channel is higher when consumers have more favorable Internet experience. In the purchase stage, consumers prefer the offline channel over the online channel, regardless of their Internet experience. Furthermore, we find that the drivers of channel preference are substantially different across the three buying stages due to (in)congruities between channel benefits desired and channel capabilities offered.
In this paper, we look at the donation behavior of donors who periodically contribute a preset amount to a particular charity. The charity firm makes extensive use of direct mail to nurture these ...donors, and in fact sends two types of mails, one that seeks to retain the donors (retention-seeking mails) and the other requesting them to upgrade their donation amount (upgrade-seeking mails). We study the different effects of the two types of direct mail on the donation behavior. To these mails, a donor has to respond by making two decisions: “should I continue donating or not?” and “should I upgrade or not?” We propose a multi-response model that accommodates not only the differential effects of the two types of mail on the donors, but also the possible correlation across the two response functions for each donor. We fit our model to a very large data set to explore whether our model can extract the unique impact of demographics and those of the two types of mail on the two aspects of donation behavior. What makes it more interesting is that the charity firm deliberately uses the observed donation behavior of people to design the two types of mail. In our model and estimation, we explicitly account for this endogenous effect to ensure that we measure the unbiased impact of the two types of mail on the two responses of the donors. Our results show that once we account for the endogenous effect and separately model the effect of different types of mail on each type of donor response, we get a much clearer picture of the “true” effects of these mails, as opposed to a simpler model that does not incorporate these effects. Firms are advised to not only carefully analyze the donation behavior of the donors but also carry out simulation exercises to understand the effects of mails in totality before taking action.
Advertising is one of the key marketing tools managers have at their disposal to influence their customers into purchasing a new product. The overall objective of new product advertising is to inform ...and persuade customers. Drawing up an advertising plan for a new product that is under the influence of diffusion phenomenon is not an easy task. Hence, research in this area is very limited. In our research, we use an empirically proven diffusion demand function that explicitly incorporates the advertising component. Our results suggest that optimal advertising is determined by the advertising effectiveness, discount rate, and the ratio of advertisement to profits. Depending upon the interplay among these factors, the optimal advertising takes decrease-increase, increase-decrease, monotonically increasing or monotonically decreasing shape.
•The research focus is on the accuracy of the peak sales time (T*) prediction.•Voice Over Noise (VON) is the proposed metric to help assess the accuracy.•We empiricallydemonstratethe effectiveness of ...VON relative to other metrics.
Managers dealing with new products need to forecast sales growth, especially the time at which the sales would reach the peak, known as the peak sales time (T*). In most cases, they only have a few initial years’ data to predict T*. Although product managers manage to predict T*, there is no method to date that can predict T* accurately. In this paper, we develop a new metric based on the diffusion modeling framework that can help in assessing the prediction accuracy of T*. This metric is built on the premise that observed sales growth is affected both by the force that systematically varies with time and by the non-systematic random forces. We show that the two forces must be carefully combined to assess if a predicted T* is accurate enough. In addition, we empirically prove the efficacy of the proposed metric.
The literature on cross-national diffusion models is gaining increased importance today due to the needs of present day managers. New product sales growth in a given nation or society is affected by ...many factors (Rogers 1995), and of these, sociocontagion (or word of mouth) has been found to be the most important factor that characterizes the diffusion process (Bass 1969, Moore 1995). Hence, it is interesting and perhaps challenging to analyze what would happen if a new product diffuses in parallel in two neighboring but culturally different countries. Not only will we expect the diffusion process in the two countries to be different, but we will also expect some interaction among them, especially if the two societies mingle with each other. There are two streams of research in cross-national diffusion. The first type focuses on exploring the differences between diffusion processes in two countries and finding out whether those differences can be attributed to social and cultural differences between the countries involved. Examples of this type of research are found in Takada and Jain (1991), Gatignon et al. (1989), Helsen et al. (1993), and Kumar et al. (1998). These studies did find some relationship between the cultural differences of the countries studied and the differences in the diffusion process. The second stream of research focuses on modeling explicitly the interaction between the diffusion processes in two countries. The interaction is typically captured through lead-lag effect (Eliashberg and Helsen 1996, Kalish et al. 1995), where the sales process in the lead country (i.e., the country where the product was first introduced) is modeled to affect the sales process in the lag country (i.e., the country where the product was introduced a few years later).
Another method to study the interaction among the diffusion processes in two countries was suggested by Putsis et al. (1997), who used a “mixing model” to empirically explore the existence of such interactions. These studies basically observed that, when a new product is introduced early in one country and with a time lag in subsequent countries, the consumers in the lag countries learn about the product from the lead country adopters, resulting in a faster diffusion rate in the lag countries. Ganesh and Kumar (1996) formulized this effect as the learning effect and, subsequently, Ganesh et al. (1997) found this learning effect to be influenced by country-specific factors (cultural similarity, economic similarity, and time lag elapsed between the lead and the lag countries) and product-specific factors (continuous vs. discontinuous innovation and the presence or absence of a standardized technology). A careful analysis of the extant literature on the second stream of research would reveal that neither the learning effect model nor the mixing model can be modified to accommodate the other model. Our contribution to the literature exactly addresses this point.
In this paper, an alternative framework is proposed that has two unique features. First, the framework is flexible enough to not only account for the lead country affecting the lag countries and vice versa, but also to accommodate the simultaneous interaction among countries in explaining the diffusion processes in the countries concerned. Using multiple product categories and a variety of new product introduction situations, we empirically demonstrate the flexibility and efficiency of our proposed framework. We found strong evidence of all types of interactions, namely, lead lag, lag lead, and simultaneous, which evidence suggests that one cannot afford to omit any of the interactions. The second unique feature of our paper is the estimation procedure that we used. Because statistical estimation of a dynamic process that includes lead-lag, lag-lead, and simultaneous types of causality within a single framework is not straightforward, we suggest an iterative estimation procedure for the estimation. This new procedure not only proved to be flexible in accommodating different types of interaction, but also converged rather quickly in all of the cases that we empirically tested. Noting that the statistical properties of these estimators are not generally available, we carried out a simulation exercise that clearly revealed the efficiency of the proposed estimation procedure. After analyzing the interaction, we went further and showed that the magnitude of the cross-national influences is affected by certain country-specific and product-specific factors. The flexibility of the proposed method over the existing methods is demonstrated through obtaining superior forecasts with the proposed method. Several interesting insights for managers concerned with formulating international marketing strategies are offered.
Loyalty programs are very common in practice. Many researchers have worked at understanding the impact of loyalty programs on market competition and the mechanism behind it. Interestingly, almost all ...of the studies have explored a symmetric equilibrium where both of the competing firms offer a loyalty program. To our knowledge, the extant literature has not investigated in-depth whether asymmetric equilibrium can exist where only one firm chooses to offer a loyalty program and the other firm chooses to compete via lowering prices. Such a question is important because some markets do support such asymmetric equilibriums with respect to loyalty programs. Also, the existence of asymmetric equilibrium shows that a loyalty program need not be profitable for some firms. In this paper, we use a game-theoretic framework to investigate specific types of customer loyalty programs that provide benefit to loyal customers in the form of discount over market prices. The model considers consumer switching and includes two types of consumer heterogeneity. The first type of heterogeneity concerns the differences between customers with respect to their liking for loyalty programs, and the second type concerns the differences among the loyalty program members with respect to their ability to collect enough loyalty points to redeem loyalty rewards. By analyzing a duopoly market, we find that both symmetric equilibrium (i.e., where both competing firms offer the loyalty program) and asymmetric equilibrium (i.e., where one firm alone offers the loyalty program) can be sustained. The paper explores conditions for the existence of these two equilibriums. PUBLICATION ABSTRACT
In this paper, we provide theoretical arguments and empirical evidence for how Genetic Algorithms (GA) can be used for efficient estimation of macro-level diffusion models. Using simulations we find ...that GA and Sequential Search-Based-Nonlinear Least Squares (SSB-NLS) provide comparable parameter estimates when the data including peak sales are being used, for a range of error variances, and true parameter values commonly encountered in the literature. From empirical analyses we find that the forecasting performance of the GA estimates is better than that of SSB-NLS, Augmented Filter, Hierarchical Bayes, and Kalman Filter when only pre-peak sales data is available for estimation. When sales data until the peak time period are available for estimation, SSB-NLS is able to obtain parameter estimates when the starting values provided are the estimates from using GA. The estimates from GA are not biased and do not change in a systematic fashion when post-peak sales data are used, whereas the estimates from SSB-NLS are biased and change in a systematic fashion. Summarizing, we find that GA may be better suited for diffusion model estimation under the three conditions where SSB-NLS has been found to have problems.
Business-to-Business (B2B) services companies invest heavily in acquiring very expensive assets that they hire out to serve their clients (e.g. UPS buys huge warehouses and hires them out to ...companies), and hence they engage in careful long-term planning and forecasting, especially when it concerns a new market. It is interesting to note that the client-firms, on the other hand, decide to hire those assets based mostly on the prevailing short-term market forces. Hence, it is important for the companies which provide the assets for hire to also build the prevailing short-term market trends into their long-term forecasting and planning. In this paper, we develop a model for tracking these two simultaneously evolving and interacting patterns, namely the asset-availability (i.e. supply) and utilization (i.e. demand) patterns, in order to better understand the underlying processes, and thereby provide a basis for better forecasting. We test our models using three sets of data collected from the oil drilling industry, and find the proposed model to provide a good fit and forecasting efficiency.
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