Pricing financial services innovations - Springer Link

Original Article

Pricing financial services innovations Received (in revised form): 24th May 2012

Mohammad G. Nejad* is Assistant Professor of Marketing at the Graduate School of Business, Fordham University. He received his PhD from the University of Memphis. His research interests relate to diffusion of innovations, particularly how firms can direct their marketing activities to use complex interactions among consumers and increase the chances of their new products’ success in the market. His research has been published in the European Journal of Operational Research and Services Marketing Quarterly.

Hooman Estelami* is Professor of Marketing at the Graduate School of Business, Fordham University. He received his PhD in Marketing from Columbia University and his MBA from McGill University. His areas of research specialization are financial services marketing, customer service management, and pricing. He has published over 30 research papers in journals such as the Journal of Retailing, the Journal of the Academy of Marketing Science, the International Journal of Research in Marketing, the Journal of Business Research, the Journal of Product and Brand Management, the Journal of Services Marketing, the Journal of Service Research, the Journal of Financial Services Marketing, Marketing Education Review, and the International Journal of Bank Marketing. Dr Estelami is the associate editor of the Journal of Product and Brand Management and the author of two books: Marketing Financial Services and Marketing Turnarounds. He has received multiple awards for his teaching and research and has advised a wide range of financial institutions on target marketing, pricing, and service enhancement strategies. *Both authors contributed equally to this paper

ABSTRACT The number of innovative financial solutions introduced to markets has grown considerably in the past decade owing to emerging digital technologies, deregulation and market fragmentation. Examples are abundant in the worldwide markets for insurance, credit products and transaction processing services. A question of growing interest is how firms should price these innovations. The optimal introductory pricing of financial innovations may vary as a function of factors such as price sensitivity of the market and competitors’ ability to introduce competing financial solutions. In this article, we examine the role of these factors in the optimal pricing of a financial innovation. Using an agent-based simulation framework, introductory pricing strategies that maximize profitability under various market conditions are identified. Results indicate that lower levels of market price sensitivity and longer time horizons for competitive entry create pricing opportunities for financial innovators. However, the relationship becomes more complex as market price sensitivity increases or competitive market entry becomes more immediate. Detailed recommendations for optimal pricing of financial innovations under various market conditions are provided, and the article concludes with strategic recommendations for pricing innovative financial services. Journal of Financial Services Marketing (2012) 17, 120–134. doi:10.1057/fsm.2012.12 Keywords: pricing; innovations; financial services; diffusion; agent-based modeling and simulation

Correspondence: Mohammad G. Nejad Graduate School of Business Administration, Fordham University, 113 West 60th Street, New York, NY, USA E-mail: [email protected]

© 2012 Macmillan Publishers Ltd. 1363-0539 Journal of Financial Services Marketing Vol. 17, 2, 120–134 www.palgrave-journals.com/fsm/

Pricing financial services innovations

INTRODUCTION Financial services markets have witnessed the introduction of a growing number of innovations during the past decade. Since the enactment of financial deregulatory measures in the United States, Europe and Asia, financial institutions have introduced a large number of new solutions in order to address the unique needs of customers in both corporate and consumer financial markets. The blending of new digital technologies into the new product development process for financial services has further accelerated the development and market adoption of new financial solutions. Examples can be found in markets for insurance, consumer credit and transaction processing services. Firms’ pricing strategies for introducing financial innovations must take into account the market forces that are at work during and following product launch. The nature of consumer interactions with each other through their social networks in the target market can further influence the diffusion process. The optimal introductory price of a financial innovation can also be affected by the speed with which competitors would be able to imitate the financial innovation. Although some financial innovations present significant entry barriers to competitors owing to technological hurdles or investment requirements, other financial innovations can be easily copied by competitors. The optimal pricing also impacts the speed of the diffusion process. Lower prices may lead to faster diffusion processes and therefore increase the market penetration of the innovator in advance of competitive entry to the market. The optimal introductory price of a financial innovation can also be affected by the degree of price sensitivity evident in the marketplace. The unique nature of consumers’ response patterns to prices in specific financial services markets may have strategic pricing implications that need to be considered when pricing financial

innovations. Empirical studies show that consumer price knowledge and ability to comprehend prices can be limited in certain financial services markets, resulting in market responses that can challenge the traditional views of a downward sloping demand function. The nature of a market’s price response, as captured by the price elasticity of demand, can therefore influence the optimal pricing of new financial services. In this article, we focus on the pricing of new-to-the-world financial solutions by pioneers. We use a simulation-based approach to examine the effects of various introductory pricing strategies on firm profitability under a range of market conditions. For every condition, we identify the pricing strategies that maximize profitability by conducting extensive agentbased simulation experiments. This approach enables the modeling of the complex social influences that consumers exert on each other during the market adoption process of a financial innovation – a social process that is growing in its significance owing to wide public access to social media tools. The results demonstrate that the optimal pricing strategy should be informed by measures of market price sensitivity as well as expectations regarding the timing of competitive entry. The article concludes with a discussion of the findings and directions for future research.

THE MARKET IMPACT OF FINANCIAL INNOVATIONS It is estimated that in the past decade alone the number of new products introduced to the marketplace outpaced the number of new product introductions for most of the previous century (Fortin and Uncles, 2011). In financial services, this growth can be partially attributed to deregulatory measures that came into effect at the turn of the century in the United States, Europe and Asia. In the United States, deregulation under the general umbrella of the Bank

© 2012 Macmillan Publishers Ltd. 1363-0539 Journal of Financial Services Marketing Vol. 17, 2, 120–134

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Modernization Act of 1999 enabled financial institutions to participate in markets from which they were previously banned. For example, deregulation allowed commercial banks to participate in the underwriting of insurance, and it also enabled insurers to sell deposit and credit products, which were traditionally sold by retail banks. An additional effect of the deregulatory mindset was the worldwide relaxation of restrictions on the development of new financial products. This, in combination with the explosive growth of the Internet and leapfrogging advancements in mobile phone technologies, has resulted in the introduction of a great number of pioneering financial solutions to the marketplace in the past decade. Examples can be found in insurance markets, where very well-defined insurance products have been introduced to address very specific risk-protection needs. For example, new insurance products have been introduced to protect the needs of athletes in certain high-risk sports (for example, ski-pass insurance), or to protect event organizers from the potential risk of event cancellations due to terrorist activities (Estelami, 2006). It is important to note however that the growing number of financial innovations in recent years has also caused concerns over the limited ability of consumers to comprehend the nature of financial offers, and has put into question the extent of responsibility and accountability that should be owned by regulators in assessing the societal impact of financial innovations (Warren, 2008; Richards, 2009). Research indicates that the complexity of financial services can at times make it difficult for consumers to evaluate a financial offer objectively. For example, financial services prices are often multi-dimensional, difficult to understand and pose challenges for consumers in determining the expected layout of funds associated with their purchase (Estelami, 2009). As a result, consumer

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knowledge of prices and their understanding of the benefits of financial services is limited, and these limitations can become profound in cases where no prior consumer exposure to the financial service exists, as one would expect in the context of financial innovations. Historically, financial innovations have been fueled by the emergence of market segments with unique financial needs not served by conventional financial solutions available in the marketplace. For example, Bank of America first introduced the BankAmericard (eventually renamed Visa) in 1958 in order to serve the unique transaction-processing needs of drivers who are frequent travelers and use specific gas stations and hotels in their travel paths (Manning, 2001). By establishing the Visa payment network at these outlets, the credit card category came to life. Considered by some as the most innovative financial innovation of the twentieth century, it is argued that this innovation has had more impact on western society than any other financial service (Manning, 2001). The launch pricing of BankAmericard comprised collecting yearly membership fees from cardholders and collecting additional network usage fees from the participating merchants. These fees were considerably higher than current credit card fees, where most cards now no longer charge membership fees, and network usage fees for merchants have dropped significantly due to competition. However, the choice of how to price an innovative financial service is not an obvious one. Although for some financial innovations a high introductory price may be a reasonable strategy, it may prove catastrophic for other innovations or under other market conditions. The optimal launch price of a financial innovation may, for example, be affected by the level of consumer price sensitivity, the anticipation of competitive market entry and the nature of the adoption process in the marketplace.



FACTORS THAT INFLUENCE INTRODUCTORY PRICING OF PIONEERING FINANCIAL SERVICES Identifying the optimal introductory price for an innovative financial service has become an important concern in recent years owing to evolving market characteristics. Consumers have become more cost-conscious as a result of increased competition, a global economic slowdown and limited household discretionary spending budgets. The emerging market environments show signs of more controlled consumer spending and increased price sensitivity. In addition, the growing power of the consumer, resulting from the mass use of social media, has influenced the rate at which consumers in the marketplace adopt innovations, financial or otherwise. Owing to the use of social media, information on new products and services can disseminate through a target population at speeds that far exceed those experienced a decade earlier. As a result, financial innovations that have great appeal to consumers can reach market acceptance more quickly with consequences on the optimal pricing of such innovations. In this section, we will examine the potential role of market price sensitivity and the expected timing of competitive entry in launch pricing decisions. The possible influence of these factors will be examined, following which a simulation-based approach to determining the optimal launch price will be presented.

The market adoption process Innovation diffusion is the ‘the spread of an innovation across markets over time’ (Chandrasekaran and Tellis, 2007, p. 40), which occurs as potential adopters learn about the new product and make their adoption decisions. According to diffusion theory, potential consumers learn about an innovation through marketing activities of sellers and the social influence exerted by other consumers (Rogers, 2003; Muller et al, 2010). During early diffusion stages, some

customers adopt an innovation because they are influenced by marketing activities or because they are in touch with the latest advances. Over time, these adopters will communicate their experiences to others through word-of-mouth (WOM) or other means of social influence, leading to further adoptions by other consumers over time (Rogers, 2003). Consumers communicate with each other and exchange WOM through their social ties. Social networks comprise individual consumers – referred to as nodes – and social ties. In a social network, consumers are connected to each other through their social ties, which connect them to their peers (Van den Bulte and Wuyts, 2007). In recent years, the social networking effect has further strengthened owing to the growing use of social media and Internet sites that enable individuals to connect with each other and learn from each others’ experiences. User reviews posted on the Internet inform the public on consumers’ experiences with a new financial service and can be disseminated very quickly within the target population. The social networking effect may have both positive and negative effects on market reactions. For example, consumer protests in reaction to price increases in the retail banking markets in the United States were communicated using Facebook, YouTube, Twitter and other social media sites (Harrington, 2011). The wide use of social media quickly resulted in the depletion of the brand positioning of some of the largest national banks in the United States and a reversal of highly contested pricing policies. In some financial services, the diffusion process is driven by the accessibility of the underlying technology that makes the service possible. This helps create a natural social network of consumers that further enhances the adoption rate of the financial innovation within the target market. For example, in order for the BankAmericard to be adopted by the consumer base, a minimum threshold number of gas stations and hotels had to


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have adopted the capabilities of transaction processing through its payment network. This threshold is often referred to as the tipping point and reflects the minimum level of market acceptance needed before the mass adoption of the innovation can become possible in the marketplace. Reaching and exceeding the tipping point was also critical to the success of PayPal, for which a certain minimum number of members would have been needed in order for it to become a widely accepted means for online financial payment processing (Gladwell, 2002).

The effects of market price sensitivity For an innovative financial service, the optimal introductory price may be affected by the way in which individual consumers interpret the price. Research in behavioral decision making shows that human financial decisions and price reaction often do not follow rational economic models (Lowenstein and Thaler, 1989). For example, when evaluating the trade-off decisions between present and future consumption of financial assets, most consumers apply discount rates significantly different from those used in financial markets. In such decisions, most consumers pay higher-than-market discount rates to secure access to immediate consumption, while demanding only low discount rates for consumption decisions in the distant future. This phenomenon, often referred to as hyperbolic discounting, has been considered as a primary driver for massive consumer debt accumulation related to excessive discretionary spending and the under-investment of the public in retirement plans – a phenomenon observed in many advanced economies (Shleifer, 2000; Murthi et al, 2011). Furthermore, consumer knowledge of financial services prices has been shown to be weak, and their ability to objectively compare financial prices suboptimal (Estelami, 2005). The lack of ability to understand financial services offers and the associated prices has in

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some markets resulted in low levels of price sensitivity. Consumers’ inability to gauge the quality of some financial services can result in the use of price as an indicator of quality. This phenomenon reduces consumer sensitivity to price, as higher prices can be considered beneficial as they would be associated in the consumers’ mind with higher levels of quality. This behavior is more evident in categories of financial services where objective quality can be difficult to establish, such as property-andcasualty insurance, life insurance and financial advisory services (Dusansky and Koc, 2010), and less evident in commoditized financial services, such as mortgages or credit products (Estelami, 2005). A financial services provider’s knowledge of the price sensitivity expected for an innovative financial service can have significant effects on the choice of the introductory price. If the consumer price sensitivity is low, then a low launch price strategy may leave potential profits untapped and also deplete market perceptions of the innovation by implying low quality. This is an especially important consideration as consumers have not seen the new-to-the-world innovation before and, as a result, they may have no prior product quality information to rely on. In such a case, price may serve as their primary information cue with respect to quality. On the other hand, if the level of price sensitivity is high, then a lower price will attract a larger mass of the target market. Given this relationship, the optimal introductory pricing depends on the balancing point between the financial effects of a lower price and the possible gains in customer count. The magnitude of the price elasticity will determine if the customer count gains resulting from a low price can have positive financial effects for the firm. A low introductory price strategy can also help the innovator secure a long-term pioneering advantage for the financial innovation, such as those resulting from the establishment of a strong brand name or the



perception of uniqueness associated with the innovation. As a result, other financial services providers may have difficulty entering the market and establishing their own market identity, or may choose to delay their entry to the marketplace.

The effects of the expected timing of competitive entry The optimal introductory pricing of a financial innovation can also be affected by the length of time it takes for competitors to introduce their own financial solutions that match the capabilities of the innovation. Some financial solutions may be protected from competitive entry because of entry barriers. Barriers to entry, such as technological hurdles, large upfront investments and patents, may prevent competitors from entering the market created through a financial innovation. For example, developing a payment network infrastructure to match that of Visa in the 1960s took competing firms over a decade (Manning, 2001). On the other hand, for some innovative financial services, entry barriers may be minimal. In cases where no major technological leaps are needed or investment requirements for launching products into a newly developed market are not high, early competitive entry is more likely to take place. Innovations in some sub-categories of insurance markets would be good examples, whereby similar insurance policies to those of the pioneer can be introduced by competitors who replicate the contractual terms of the pioneer’s insurance product. Variations in the timing of entry of competitors in a newly developed market may also result from variations in customer retention rates. Research indicates that in financial services markets the tendency of customers to switch to competitors is generally low (Panther and Farquhar, 2004; Dawes et al, 2009). Therefore, even after the entrance of competitors, adopters of a financial innovation have a low propensity to switch to competitors. This further reduces

the financial incentive for the competition to enter the market. Competitive entry may be further delayed owing to regulatory requirements, which prevent a massive influx of competitors at local, regional or national levels. For example, in the United States, where insurance products are regulated at the state level, specific requirements and restrictions exist for each state, with respect to the introduction of new insurance products. The expected timing of competitive entry can affect the overall marketing strategy of a financial innovation. However, this issue is not unique to financial services and is a well-documented concern in many markets, especially those of a high-technology nature where the product’s life cycle can be short. For some technology firms, a skim-andwithdraw strategy is often adopted in cases where competitive entry is anticipated within a short time of the launch of the innovation (Walker et al, 2010). In this strategy, high prices are charged upon product introduction and once competing firms enter the market a withdrawal market strategy is adopted whereby prices are dropped, or the pioneer withdraws from the market altogether. For some financial innovations, an initial price skimming strategy may also be an appropriate approach as barriers to entry may be minimal and imitation by competitors may occur within a short period of time. The effects of the timing of competitive entry will therefore be examined through the simulation framework that is discussed in the next section.

METHODOLOGY Simulation modeling is a viable approach for examining diffusion phenomena that are difficult to examine using other methods, and has been shown to have a high degree of internal validity (Davis et al, 2007; Harrison et al, 2007). In this approach, a market environment is modeled and the expected business outcomes, such as profits and market share, are computed. The process


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is repeated multiple times, under a variety of market scenarios, in order to develop an understanding of variations in the business outcomes resulting from changes in the inputs (for example, introductory price, level of market price sensitivity, timing of competitive entry) as well as natural randomness in the underlying market adoption process (North and Macal, 2007). We employ an agent-based modeling and simulation (ABMS) approach (Bass, 2004; Muller et al, 2010) to explore the research questions outlined above. ABMS provides a setting for simulating consumers as autonomous entities who can interact with each other through their social ties (North and Macal, 2007) – a phenomenon of growing significance in light of the explosive use of social media by the general population. The methodology also allows for longitudinal examination of the diffusion process and the assessment of the impact of the study factors on diffusion outcomes. At the heart of the simulation is a diffusion process reflected in the Bass diffusion model. The Bass model is popular because it is parsimonious and has high predictive capability as supported by over 40 years of empirical research (Bass, 2004). It uses two parameters for modeling innovation diffusion: parameter p – capturing the overall effects of the marketing activities of the innovating firm – and parameter q – capturing the influence of adopters on other consumers (Bass, 1969). The Bass model was initially introduced to examine the diffusion of durables but has since been successfully used for modeling diffusion processes for a wide range of innovations in various service industries (for example, Mesak and Darrat, 2002; Bass, 2004; Jiang et al, 2006). Examples are market adoption of online banking services (Hogan et al, 2003), Cable TV (Lilien et al, 2000) and cellular phones (Krishnan et al, 2000). Whereas the original Bass model examined the diffusion process at the aggregate market level, recent studies have developed individual-level

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methodologies (for example, Goldenberg et al, 2002, 2007; Ghoreishi Nejad, 2011). These individual-level models have provided new opportunities for studying the diffusion process because they allow for modeling each individual customer’s adoption decision as well as the effects of the relationships among customers, which may further influence adoption rates. We will use this individuallevel approach in simulating the adoption process. In the next section, we will discuss the consumer decision-making process and incorporate the effects of price, competitive entry timing and price sensitivity on the performance outcomes of the firm.

Consumer decision process to adopt a financial innovation In modeling the diffusion process, we use a chronological sequence for individual consumer decisions in adopting a financial innovation. When a financial innovation is first launched (time = 0), all consumers in the marketplace are in the pool of potential adopters. During the early diffusion stages, marketing activities – captured by parameter p of the Bass model – are the primary driver of innovation diffusion and may influence some consumers to adopt the innovation. These consumers will leave the pool of potential adopters and will move to the pool of actual adopters. They will also initiate WOM communications with other consumers in their social network, which increases the chances of adoption by the market (Amini et al, 2012). The effect of WOM is captured by parameter q of the Bass model. Building on earlier studies (Goldenberg et al, 2002), we calculated the probability of a potential consumer i adopting the product p(i, t) at time period t using the following formula: p( i ,t ) = 1 − (1 − pi )(1 − qi )Si ( t ) where pi captures the influence of marketing activities on each potential consumer, qi represents the WOM effect, and Si(t) is the



number of adopters at time period t who have a social tie with a potential consumer i. The next step in the model is to incorporate the impact of price on the probability of adoption. Previous aggregate-level diffusion models have assumed a multiplicative impact for price (for example, Mesak and Darrat, 2002; Lehmann and Esteban-Bravo, 2006). We incorporate this approach in modeling consumer adoption decisions by using exp( − b(price − 1)) as a multiple of the above formula, with b capturing the price elasticity of demand. Therefore, the probability of adoption by each consumer will be calculated as: p( i ,t ) ← 1 − (1 − pipr )(1 − qipr )Si ( t ) where pipr = pi exp( −b( price − 1)) qipr = qi exp( −b( price − 1)) Thus we have a zero adoption probability when price is infinite and the base level of diffusion is at the price of 1. In this study, the price of 1 is set as the base price that generates the base level of market diffusion as a benchmark, to which all other simulated market and pricing conditions will be compared. Therefore in establishing the best pricing strategy (for example, decreasing or increasing the price from this base level), financial outcomes will be compared with simulated outcomes resulting from the base price of 1. In the base case, a price of 1 denotes that every adoption generates a profit of 1 unit for the firm, and the unit variable cost is assumed to be negligible, as is often the case in most service organizations (Estelami, 2005; Oliver, 2009). The parameter values and ranges in the simulation were chosen based on previous studies. This was done in order to accurately capture the effects of real-world market characteristics. The appendix provides the empirical and theoretical bases for the choice of parameter values used in the simulation.

The dependent variable To compare the performance of different pricing strategies, the net present value (NPV) of profits was used as the dependent variable. The NPV of profits captures both the number of adopters and the timing of adoption. This is an important consideration as adoptions that take place during early stages of the diffusion process are more valuable to the firm than those which occur during later stages (Libai et al, 2010). In line with earlier studies (for example, Goldenberg et al, 2007; Ghoreishi Nejad, 2011), the performance of every pricing strategy is captured by comparing the NPV of profits resulting from the diffusion process, where the pricing strategy was applied, with that of the base case (that is, price of 1) as calculated using the relative NPV, referred to as NPVR: NPVR =

NPVPricing Strategy NPVBaseCase

For example, an NPVR of 0.7 shows a 30 per cent drop in NPV of profits compared to the profits earned in the base case where price is set to 1. Similarly, an NPVR of 1.1 indicates a gain of 10 per cent in the NPV of profits relative to the base case.

RESULTS A full-factorial design of price level, market price elasticity and expected time horizon of competitive entry was conducted. As shown in Table 1, price was varied at five levels, price elasticity was varied at four levels, and the timing of competitive entry was also varied at five levels. This resulted in 100 (that is, 5×4×5) experimental conditions. For each of these conditions, simulation runs were replicated 20 times to capture the variations in NPVR that may occur due to stochastic effects (Ghoreishi Nejad, 2011). Overall, considering all factorial combinations, the study comprised 400 experiment replications and five different expected time horizon levels


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Table 1: Simulation model parameters Parameter

Parameter value or range

Source(s) for chosen parameters

Factor 1: Unit price 4, 0.7, 1, 1.3, 2

Lehmann and Esteban-Bravo (2006); Estelami (1999)

Factor 2: Price elasticity

Bijmolt et al (2005); Lehmann and Esteban-Bravo (2006); Tellis (1998)

0.2, 0.6, 1, 2

Factor 3: Expected 3, 6, 9, 12, Unlimited time horizon

Reflecting time when approximately 3%, 16%, 50% and 84% of market adopted in the base case. The unlimited refers to the time when 95% of the market in each experiment adopted the innovation

Average number of social ties per consumer Discount rate Market size p (External influences) q (Internal influences)

14

Ghoreishi Nejad (2011); Goldenberg et al (2007); Libai et al (2010)

10% 3000 0.0142

Goldenberg et al (2007); Libai et al (2010) Ghoreishi Nejad (2011); Goldenberg et al (2007) Jiang et al (2006); Libai et al (2009); Hogan et al (2003); Sultan et al (1990)

0.545

Jiang et al (2006); Libai et al (2009); Hogan et al (2003); Sultan et al (1990)

Note: Please consult the appendix for more detailed discussion on the choice of parameters used in the simulation study.

Table 2: ANOVA model for the effects of price, price elasticity and expected time horizon Source Price Price elasticity Expected time horizon Price×Price elasticity Price×Expected time horizon Price elasticity×Expected time horizon Price×Price elasticity×Expected time horizon Error Total Corrected total

Sum of squares

DF

21.207 6.103 9.631 179.720 36.475 14.732 24.087

4 3 4 12 16 12 48

7.221 1896.829 299.177

1900 2000 1999

Mean square

F

P-value

5.302 2.034 2.408 14.977 2.280 1.228 0.502

1394.988 535.220 633.531 3940.556 599.816 323.015 132.032

0 0 0 0 0 0 0

0.004 — —

— — —

— — —

Adjusted R 2=0.975.

for each experiment replication, resulting in a data set of 2000 cases. In order to examine the effects of various pricing strategies and market conditions on profits, we conducted a price×price elasticity× expected time horizon Analysis of Variance (ANOVA), with NPVR as the dependent variable. Table 2 summarizes the findings of this analysis. The F-ratios for all the main effects and the interactions were statistically significant. As Table 2 shows, this is true for price (F(4,1900) = 1394.99, P < 0.001), price elasticity (F(3,1900) = 535.22, P < 0.001), and expected time horizon (F(4,1900) = 633.53, P < 0.001), which all have significant main effects on NPVR. Moreover, all two-way

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interaction effects and the three-way interaction effect between the study factors are significant. The three-way interaction in the ANOVA results indicates that the best pricing strategy depends on the combination of price elasticity and the time horizon for competitive entry. To help dissect the complex interaction between the factors, the effects of various pricing strategies on NPVR under different levels of price elasticity were plotted. Figure 1 provides the associated factorial plots. As can be seen from Figure 1(a), when the price elasticity is at its lowest level, a high introductory price generates the highest NPVR, for all expected



Figure 1: Three-way interaction between price, price elasticity and expected time horizon: (a) very low price elasticity (0.2); (b) low price elasticity (0.6); (c) high price elasticity (1); (d) high price elasticity (2).

time horizons of competitive entry. The average NPVR ranges from a low of 1.36 to a high of 1.75, indicating that a high price strategy can generate anywhere between 36 per cent and 75 per cent higher profits versus the profits generated from the base-case price. Furthermore, as the introductory price decreases, NPVR also decreases, indicating the negative effects on profitability of lowering prices, under this specific market condition. As Figure 1(a) indicates, when the market is not price sensitive, regardless of the timing of competitive market entry, a high price strategy would be optimal for the introduction of a financial innovation. However, the relationship becomes more complex when price elasticity increases. As shown in Figure 1(b), with a price elasticity of 0.6, firms may expect a significant increase

in NPVR only when competitive entry is expected to take a very long time, perhaps due to factors such as market entry barriers or regulations. Under this condition, charging a high introductory price for a financial innovation leads to a direct increase in the value of NPVR, exceeding the threshold of 1.0, thereby representing a profit gain versus the profits in the base-case price. On the other hand, in conditions where a more restricted time horizon for competitive entry exists, charging prices different from the base case can lead to profit changes, which in most cases may lead to decreases in the NPVR compared to the base case. The contrast in the outcomes shown in this situation versus the ones examined in Figure 1(a) demonstrates the significant effect that market price elasticity can have on the introductory pricing of financial innovations,


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and highlights the importance of conducting appropriate market research to measure price elasticity of demand before product launch. As shown in Figure 1(c), when price elasticity is 1, and a short time horizon for competitive entry is expected, a competitive pricing strategy (below the base-case price) can lead to gains in NPVR. For example, a price of 0.7 can result in NPVR values ranging from 1.03 (for a very short time horizon) to a peak of 1.19 (for a moderate time horizon for competitive entry). However, as competitive entry is delayed, such a pricing strategy may no longer be optimal and result in NPVR values below 1. In fact, when a long time horizon for competitive entry is expected, a moderately high price strategy (for example, 1.3) generates the highest NPVR. Under conditions of very high price elasticity (Figure 1(d)), optimal pricing depends on the time horizon of competitive entry. When the time horizon is short (3 or 6 periods), a very low introductory price generates the highest NPVR. However, for average to long time horizons, a moderately low price (that is, 0.7) generates the optimal NPVR. These two scenarios emphasize the significance of competitive pricing of financial innovations in highly price–sensitive markets. The contrast between these results and those examined in Figures 1(a) and (b) highlights the complexity that the timing of competitive entry can introduce to the optimal pricing of a financial innovation. For this reason, financial innovators must collect as much competitive intelligence as possible to form accurate expectations regarding the timing of the market entry of potential competitors, before finalizing launch price decisions.

DISCUSSION This research examined the optimal introductory pricing of financial innovations. A systematic analysis of what prices would maximize profitability was conducted, by examining the effects of variations in price

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elasticity and the expected timing of competitive market entry on profitability. The results demonstrate that the choice of the pricing strategy for a financial innovation may lead to significant financial gains or losses if financial innovators do not carefully consider the impact of market price elasticity and the timing of competitive entry. When high degrees of price sensitivity exist and a short time horizon for competitive entry is expected, lowering the introductory price would be an optimal strategy. On the other hand, when price sensitivity is low and the expected time horizon for competitive entry is long, higher introductory prices for a financial innovation would be the optimal choice. As shown in Figure 1, more complex relationships arise in relationship to the optimal pricing of a financial innovation, as a function of market price sensitivity and expected timing of competitive entry. These results are consistent with anecdotal evidence on the launch pricing of wellestablished financial innovations. For example, the launch of BankAmericard was not matched by competitive entry for many years. The product targeted a market segment that was not highly price sensitive and valued the unique benefits of the credit card in facilitating business travel needs. These market conditions reflect those of Figure 1(a), in which BankAmericard’s optimal strategy was to launch the product at a high price point. This was done by charging yearly membership fees, interest rates on balances from members and transaction processing fees from merchants (Manning, 2001). On the other hand, for a financial innovation where price sensitivity is high and competitive entry can be easily achieved, a different pricing strategy would prove optimal. Variations of mortgage products, such as flex mortgages and extended-life (for example, 40-year) mortgages, introduced in recent years represent good examples. Mortgage instruments can be readily replicated by competitors and market price sensitivity for



these products is relatively high, representing scenarios similar to Figure 1(d). In such markets, a low price strategy has been adopted by financial innovators who seek to benefit from the short-term gains they can realize before competitors enter the market. It is important to also recognize that the low-price strategy adopted under this market condition can also serve as a deterrent for competitors who may find it difficult to realize reasonable margins given the level of price competition, and may therefore choose to delay their market entry. This delay in competitive entry may further help the financial innovator realize incremental profits. One of the important aspects of the methodology used in this article is providing a setting for studying the social effects among consumers in the process of adopting a financial innovation. With growing reliance of the public on various forms of social media, it is important that in examining pricing strategies, the possible interactions that will occur among consumers be modeled. The diffusion framework used in this article facilitates such analysis. In addition, this study expands the existing agent-based diffusion models by incorporating price in customer decisions and applying this model to establish the optimal price of a financial innovation. This framework provides extensive flexibility and power in studying diffusion processes at the individual level, and the methodology creates new opportunities by overcoming limitations of other methodologies. For example, some of the earlier diffusion studies that have used closed-form solutions had to limit the number of diffusion periods owing to computational limitations associated such models (for example, Lehmann and Esteban-Bravo, 2006).

LIMITATIONS AND FUTURE RESEARCH This study provides insights on optimal pricing of financial innovations, and demonstrates the significant impact of market price sensitivity and timing of competitive

entry. The results show that firms may risk significant losses by ignoring these two variables, and highlight the significance of conducting appropriate levels of market research and competitive intelligence gathering before deciding on the launch price of a financial innovation. Financial innovators must therefore undertake sufficient market research to accurately measure price sensitivity levels among target consumers before deciding on an introductory price. Furthermore, research must be conducted to assess the likely timing of competitive entry into the market that the financial innovation will create. This research may take on the form of examination of prior cases of competitive entry by established competitors, interviews with industry experts or other forms of intelligence gathering. Although the study provides important insights into pricing of financial innovations, it is important to acknowledge several limitations and provide direction for future research. First, the adopted methodology is based on several assumptions that are common in studies of diffusion processes (Goldenberg et al, 2007), whereby only one type of social influence, namely, WOM effects, between consumers is considered. However, this may not represent a major limitation as consumer-to-consumer communications for innovations are typically communicated through WOM activity (Gounaris et al, 2003; Estelami, 2006; Casalo et al, 2008). Nevertheless, alternative means of social influence such as observing others’ use of an innovation or social influence through consumers’ desire to achieve specific self-image goals (Chen et al, 2011) may provide interesting opportunities for extending this work. Another limitation of this study is the focus on optimal launch pricing before competitive entry. The dynamics of market competition following competitive entry may be highly complex and depend on the nature of competitors who choose to enter a market


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established by a financial innovation. Future research can therefore examine reactive pricing strategies following competitive entry, in order to establish how financial innovators’ prices should be adjusted to competitive intrusion into the markets they helped create. However, it is hoped that this research has demonstrated the significance of careful assessment of market price sensitivity and competitive entry timing on the optimal pricing of financial innovations, in a world of growing diffusion effects resulting from mass use of social media by consumers.

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APPENDIX

categories of adopters as discussed by Rogers (2003). Furthermore, because increasing price may lead to slower adoption rates, and hence longer diffusion patterns, we also captured the dependent variable at a time when 95 per cent of the market have adopted the product. This allowed for examining conditions where the firm does not have any concern with the amount of time it may take for the diffusion process to complete and the goal is to maximize profits regardless of the time horizon.

Selection of parameter values for the simulation model Price, price elasticity and time horizon The simulation examines diffusion processes resulting from sales prices of 0.4, 0.7, 1 (base level), 1.3 and 2. In addition, price elasticity is systematically varied at four levels: 0.2, 0.6, 1 and 2. These values are in line with those used in previous theoretical studies (for example, Lehmann and Esteban-Bravo, 2006) and those found in meta-analysis of empirical studies (Tellis, 1998; Bijmolt et al, 2005). To capture the expected time of competitive entry, we applied the customer groups discussed by Rogers (2003) in the base model. Rogers categorizes adopters into five categories: innovators, early adopters, early majority, late majority and laggards. We examined the time horizons of 3, 6, 9 and 12 periods. These numbers represent the average time when approximately 3 per cent, 16 per cent, 50 per cent and 84 per cent of the market have adopted the product in the base case. These values approximately represent the cumulative adoption of the four first groups of the aforementioned

Parameters p and q The values of parameters p and q are based on the values estimated by previous empirical research and meta-analyses. As this study seeks to examine the context of financial innovations, the choices of values for parameters p and q represent average values considered for such an innovation in previous research. Thus, we fixed the aggregate-level parameter p to 0.0142 and the aggregate-level parameter q to 0.545, values estimated for the diffusion of online banking (Libai et al, 2009). These values are in line with those used in earlier studies that examined online banking (Hogan et al, 2003). Moreover, previous research has found that consumers perceive greater risk in


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the adoption of services compared to the adoption of goods, and thus adoption of services relies more heavily on WOM and personal sources of information, and less so on information from marketers (Murray, 1991). Therefore, the choice of the two parameters in this study are in line with the average values estimated in previous meta-analysis studies (Sultan et al, 1990; Jiang et al, 2006). The aggregate-level values of parameters p and q were converted to the individual-level parameters pi and qi using the methods suggested in the literature (Goldenberg et al, 2002; Toubia et al, 2008). The parameter p is the same at both aggregate and individual levels. However, the individual-level value of parameter q is calculated by dividing the aggregate-level value of parameter q by the number of individual ties. Therefore, the overall diffusion process resulting from this model are comparable to those at the aggregate level (Goldenberg et al, 2002).

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Fixed variables We fixed the average number of one-to-one connections between one consumer and other consumers to 14, a value that is in line with the average number found in previous studies (Goldenberg et al, 2007; Libai et al, 2010; Ghoreishi Nejad, 2011). For the purposes of the simulation, we fixed the market size – number of potential consumers – to 3000, a value that is in line with earlier studies (for example, Goldenberg et al, 2007; Ghoreishi Nejad, 2011). Furthermore, because the average number of connections is used in converting the aggregate-level value of q into individual-level values, it is also indirectly captured in the diffusion process through the influence of consumers on each other through qi, the number of social ties does not significantly alter the results (Ghoreishi Nejad, 2011). It is important to note that we further examined values of 4 and 24 and the results remained the same.
