Pricing practices and firms' market power in international cellular markets, an empirical study

Previous studies on international marketing have typically asked the question: “how is the demand characterized across countries?” Such analysis is then used to provide guidelines for firms to enter new markets and/or to allocate marketing resources across countries. To provide such normative guidelines, however, one also needs to analyze the supply-side of the problem, i.e., ask: “what is the likely market power that firms will be able to command in different countries?” Building on the New Empirical Industrial Organization (NEIO) framework, recent research in marketing provides marketers with a variety of models to explore competitive interactions among firms in the context of a single market. The goal of this paper is to extend this literature to a multimarket/multinational context to help international marketers assess the likely market power they face when entering new countries. We illustrate the proposed method on the mobile telecommunications industry, using price and quantity data from 10 countries around the world, estimating firms' market power as a function of a number of country characteristics.The results indicate that, while the simple presence of competition diminishes firms' market power, it does not lead to perfect competition. Interestingly, a higher number of competitors in a country does not seem to have significant incremental effect on market power. In contrast, the country's commitment to a severe antitrust policy has a significant negative effect, while the monopolist's lead-time before competition is allowed has a significant positive effect on market power. These findings, together with a change in price elasticities as a result of competition, suggest that market power in different countries may originate from two sources: (i) collusive pricing among cellular operators and (ii) consumers' switching costs across service providers. For international marketers, the findings imply that the attractiveness of wealthier countries (with usually faster diffusion rates and larger market potential) may be mitigated by higher levels of competition (as a result of developed antitrust regulation and more consumer exposure to competitive marketing practices). From a policy point of view, it suggests that (in contrast to the conventional wisdom) simple deregulation may not be enough to reduce prices to competitive levels. In addition, a severe antitrust policy is crucial to achieve this goal.     

Advertising to a social network

Direct advertising—sending promotional messages to individual customers—is increasingly used by marketers as a result of the recent improvements in consumer reachability. Most current methods to calculate optimal budgets for such advertising campaigns consider customers in isolation and ignore word-of-mouth communication (WOM). When the customer base forms a network (as is the case in telecom or social network databases) ignoring WOM clearly leads to suboptimal advertising budgets. This paper develops a model to help address this challenge. We assume that firms know the network structure formed by customers but do not know (or are not allowed to use) data on individuals’ connections. Under this scenario, we compare the optimal campaign of a monopolist to that of firms competing in simultaneous-move or sequential-move games. The analysis provides two key insights: (i) we show that ignoring the existence of WOM leads to significant profit loss for firms and this is more so under competition. In particular, knowing the “density” of consumer connections is crucial for the design of optimal campaigns. (ii) Competition in direct advertising exhibits strong first-mover advantages and, even in a simultaneous-move game between identical firms, highly asymmetric outcomes are possible. The paper also explores two extensions. First, we study the nature of the trade-off between increasing network size versus increasing the connectivity between existing network members. Second, we investigate how firms’ advertising activity may endogenously grow the membership base.     

Adoption Dynamics in Buyer-Side Exchanges

The purpose of this paper is to understand buyer/seller adoption dynamics in independent, buyer-side B2B exchanges. In a stylized model, we assume that the main role of the exchange is to reduce search costs for buyers. Buyers and sellers enter or exit the exchange based on the relative economic surplus (loss) they receive inside vs. outside the exchange. We contrast two situations: one where participants' switching cost to join the institution is negligible and another, in which it is significant. In an extension, we also explore the impact of buyer/seller heterogeneity on adoption dynamics. We have three key findings with relevant implications for practice. First, we find that the general view that demand and supply (so-called liquidity) either grows or shrinks in the marketplace may not hold. In the presence of switching costs, the exchange can evolve to a stable state with only partial market participation. Second, our results suggest that the exchange is better off subsidizing buyers as opposed to sellers in order to achieve the so-called critical mass, beyond which there is full participation. Finally, we find that while in general, minor buyers of the industry have more incentive joining the exchange, when the fixed participation fee of the exchange is high, it is major buyers who are likely to join first. For sellers, this is not the case: minor sellers are always more keen in participating in a buyer-side exchange.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Buying and selling information under competition

Markets for information products exhibit varying degrees of competition on both the supply and the demand side. This paper studies the potential complementarity of information products, equilibrium information buying behaviors and information price setting in such markets. Our game-theoretic model consists of two information providers selling imperfect information to two competing clients and allows for different information quality levels as well as varying degrees of client competition. Absent of client competition, information providers compete on the statistical properties of the information they supply (i.e., the accuracy of the information). The competitive price can be high because of potential complementarity among information products when these are not very reliable. However, this may change when the clients are competing against each other. We adopt a reduced-form model of buyer competition that reflects situations where information buyers face discrete alternatives. We find that a buyer gains more through information acquisition when its competitor is less informed, suggesting a first mover advantage in information acquisition. More importantly, we also find that intense client competition can make the information products more substitutable, resulting in a lower equilibrium price for information. Furthermore, this effect leads to harsh competition between information providers and consequently provides incentives for exclusive contracting. In summary, it is found that the “quality” of information has a very different impact on sellers’ profits depending on the degree of client competition.     

The acceptance and adoption of continuous auditing by internal auditors: A micro analysis

The umbrella of “advanced technology” covers a range of techniques widely used in the U.S. to provide strategic advantage in a very competitive business environment. There is an enormous amount of information contained within current-generation information systems, some of which is processed on a real-time basis. More importantly, the same holds true for actual business transactions. Having accurate and reliable information is vital and advantageous to businesses, especially in the wake of the recent recession. Therefore, the need for ongoing, timely assurance of information utilizing continuous auditing (CA) and continuous control monitoring (CM) methodologies is becoming more apparent. To that end, we have conducted interviews with 22 internal audit managers and 16 internal audit staff members at 9 leading internal audit organizations to examine the status of technology adoption, to evaluate the development of continuous auditing, and to assess the use of continuous control monitoring. We found that several companies in our study were already involved in some form of continuous auditing or control monitoring while others are attempting to adopt more advanced audit technologies. We also made a large number of surprising observations on managerial, technology training and absorption, and other issues. According to our audit maturity model, all of the companies were classified between the “traditional audit” stage and the “emerging stage,” not having yet reached the “continuous audit” stage. This paper,¹ to our knowledge, is the first to study CA technology adoption in a micro level by an interview approach.     

An evidential reasoning approach to Sarbanes-Oxley mandated internal control risk assessment

In response to the enactment of the Sarbanes-Oxley Act 2002 and of the release of the Public Company Accounting Oversight Board (PCAOB) Auditing Standard No. 5, this study develops a risk-based evidential reasoning approach for assessing the effectiveness of internal controls over financial reporting (ICoFR). This approach provides a structured methodology for assessing the effectiveness of ICoFR by considering relevant factors and their interrelationships. The Dempster-Shafer theory of belief functions is utilized for representing risk.First, we develop a generic ICoFR assessment model based upon a Big 4 audit firm's approach and apply it to a real-world example. Then, based on this model, we develop a quantitative representation of various levels of ICoFR effectiveness and related risk-assessment as defined by the PCAOB and contrast these representations with levels implied by Auditing Standard No. 5. In doing so, we demonstrate the potential value of formal risk assessment models in both facilitating the assessment of risks in an individual engagement and in assessing the effects of different regulations.     

EDP auditing instruction using an interactive generalized audit software

Most auditing curricula do not provide students with experiential reinforcement of auditing and/or computer auditing concepts. This paper reports on an interactive generalized audit software package that has been designed to fill this educational void. The TREAT package consists of four different modes of operation with four audit eases that enable students to gain hands-on audit experience. The features of the TREAT package are similar to many accounting firms generalized audit software packages. Students using the package and its audit cases have proven TREAT to be an effective and efficient pedagogical tool.     

The case for process mining in auditing: Sources of value added and areas of application

Process mining aims to extract knowledge from the event logs maintained by a company's ERP system. The objective of this paper is to make the case for why internal and external auditors should leverage the capabilities process mining offers to rethink how auditing is carried out. We do so by identifying the sources of value added of process mining when applied to auditing, which are as follows: 1. process mining analyzes the entire population of data and not just a sample; 2. critically that data consists of meta-data—data entered independently of the actions of auditee—and not just data entered by the auditee; 3. process mining allows the auditor to have a more effective way of implementing the audit risk model by providing effective ways of conducting the required walkthroughs of processes and conducting analytic procedures; 4. process mining allows the auditor to conduct analyses not possible with existing audit tools, such as discovering the ways in which business processes are actually being carried out in practice, and to identify social relationships between individuals. It is our argument that these sources of value have not been fully understood in the process mining literature, which has focused on developing it as a statistical methodology rather than on applying it to audit practice. Only when auditors and audit researchers appreciate what is new and unique about process mining will its acceptance in auditing practice become feasible.     

How incumbent firms foster consumer expectations, delay launch but still win the markets for next generation products

Consumers learn quality of many durable products through word-of-mouth information while firms launch new and improved products frequently in these markets. This paper examines firm incentives to invest in R&D to compete for patents in makets where consumers rely on word-of-mouth information and have expectations about the new products before launch. When its loss due to a possible entry is above a threshold, an incumbent has more incentives than a potential entrant to invest in R&D for patents. Moreover, if the current product is more profitable, its true quality is above consumer priors and the quality of the new product is below a threshold, it is optimal for the incumbent to launch the new product after a time lag. The later the optimal time of launch, the greater is the incumbent’s potential loss if entry occurs and greater its incentives is to invest in R&D versus that of the entrants. While potential entrants are generally thought to have more incentives to invest in a drastic innovation which results in a race to launch the new products, we show that the more drastic the innovation, the later the optimal time of launch and greater are the incumbent’s incentives to invest in R&D when the value added of the new product can be conveyed to all the consumers. Only when consumers are uncertain about the value added of the new product, the incumbent’s incentives are lower. We also demonstrate that by promoting consumer expectations about the new product before launch, an incumbent has more time to launch and higher probability of dominating its market.