Abstract

     Since the COVID-19 pandemic arrived, various options have been created to export products, with the creation and development of startups becoming a viable and significant option. Previous research has described the influence of competitive business advantage on international performance. However, the novelty of the current research was to evaluate the influence of the company’s capabilities, through the competitive advantage of the company as a mediator in international performance. The study was based on the responses of 200 active startups in Peru. The PLS-SEM technique was utilized with the company’s capabilities linked to internationalization processes as independent variables, as well as market intelligence, product innovation, and pricing. The mediating variable was competitive advantage, and the dependent variable was international performance. It was found that all three capabilities influenced competitive advantage and, ultimately, international performance. It was concluded that competitive advantage does not act as a mediator between pricing capabilities and the international performance of startups. Moreover, it was determined that competitive advantage acts as a mediator between two of the three assessed capabilities: market intelligence capabilities and product innovation capabilities. The results were supported by the reflective and formative evaluation of the results obtained through the applied technique. These results could help create better programs to promote startups interested in internationalizing and exporting products.

Introduction

     In the last 30 years, the resource-based theory (RBT) [1] has been shown as one of the most relevant for the description, explanation and prediction organization. It should be noted that RBT only took shape in the 1990s, because in the 1980s, theories were focused on the outside of organizations, such as Porter’s forces. In the first decade of the new century, the application of RBT could be supported in different disciplines. Today, RBT can be recognized as an influential approach in strategic management. The framework of RBT is composed of four elements: (a) value, (b) being rare, (c) immobility, and (d) sustainability. These are the characteristics that a company needs to have for the strategic planning reference and hold a sustained competitive advantage. The four conditions of RBT suggest that poor organizational policies, processes and procedures can weaken a resource’s potential competitive advantage.

     There is also the concept of dynamic capabilities (DC), which can be considered an extension of the resource-based view (RBV) because of its ability to respond to rapid technological changes. Although this concept was born at the end of the 1950s, it was through the publication of Teece, Pisano and Shuen [2] in 1997 that it gained prominence in strategic management. DC is defined as “the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments” [2]. First of all, dynamic capabilities can be conceptualized as those meta-capabilities that give companies the ability to adapt to new changes and that at the same time can be specific to the company, i.e., very difficult to imitate. There are six functions that can be performed by DCs to enter a new market or position themselves more strongly in a market already won: renew, recombine, redeploy, replicate, retrench, and retire the resources/capabilities. With this, it can be identified that it is not only a matter of creating resources, but also of eliminating those that do not contribute to the creation of competitive advantage. There is no consensus on when to use DC, since on the one hand, it can be argued that it works well in a scenario of rapid changes, especially technological ones, but it can also be argued that in non-repetitive changes, its use is not advisable because it could be reconfiguring the company’s resources for a very short period, and its benefit could be minimal. The organization who possesses DCs can meet the change that is necessary to build a competitive advantage. Both RBT and CD contain elements that are crucial to a company’s competitive advantage. On the one hand, the value of the processes, products and services that an organization must have must be identified, understood and measured, so that it can be known why the company’s capabilities are valuable. Rarity is also identified, i.e., these capabilities are unique in the marketplace; it is also expected that they cannot be copied by competitors. Dynamic capabilities need the company’s continuous work to make them evolve to ensure that they remain rare and inimitable, achieving such a positioning that they are not substitutable.

Results

     As previously mentioned, we performed PLS-SEM to examine our hypotheses, for this, a bootstrapping was used of 5000 subsamples, this technique was performed used to estimate summary statistics, such as the mean or standard deviations, considering that it is the most approved setting for PLS-SEM [137]. As seen in Table 5, the capabilities of product innovation, marketing intelligence, and price are firmly related to the variable of competitive advantage; in other words, hypotheses H1, H2, and H3 are supported. Additionally, Table 5 shows that competitive advantage is also firmly connected to the international performance of startups; as a result, hypothesis H4 is also supported. The variance inflation factor (VIF), which was also examined to identify if our result has multicollinearity problems, demonstrates that it is not a problem between the exogenous latent constructs. Every VIF value that is not a problem is between 1 and 2, below 5. In the case of the R square values in Table 5, our model explained 29.2% of the competitive advantage variance and 16.2% of the international performance of the startups, which means that this model fits our data very well because the predictive relevance confirms the rule of thumb that says it must not be negative [137].

1. Introduction

2. The data

3. Methodologies: the SVAR models and copula modeling

4. Results

5. Concluding remarks

Appendix A. Supplementary data

References

Abstract

Following the adoption of new techniques of shale and fracking by U.S. oil companies, a structural vector autoregression model (SVAR) complements studies on why Brent and WTI started to diverge around early2011. Using monthly data from 2000 to 2018, we decompose oil supply into: world oil (excluding U.S.), U.S. conventional (non-tight) oil and U.S. tight oil. We examine the variance decomposition of stock returns for the aggregate market (S&P 500), the S&P Energy sector and Chevron and Exxon Mobil oil companies, and we further identify differences between two subsamples from 2000 to 2010 and 2011 to 2018, respectively. We find that supply considerations (especially due to tight oil) become more important in the subsample after 2011, not only for individual oil companies but also for the aggregate market and energy sector: Supply shocks due to tight oil explain in our benchmark model between 29% (S&P 500) and 31% (S&P Energy) of the variance in stock returns after 24 months and between 28% and 29% for oil companies. None of these are statistically significant in the pre-2011 subsample. Among impulse responses, tight oil production responds positively to disruptions in world oil, and U.S. stock returns respond positively to oil price shocks and respond negatively to tight oil shocks which is a further finding while being consistent with the literature. Copula modeling uncovers stronger tail dependences in the second subsample for the interactions during downturns and upturns among global demand, crude oil prices and stock markets.

Introduction

In an influential paper, Kilian (2009) proposes a structural vector autoregression (SVAR) approach to global oil supply, an index of global real demand and real oil prices. The latter are typically calculated by oil prices denominated in U.S. dollars (such as the U.S. refiner cost of oil, crude WTI or Brent oil prices) deflated by the U.S. price index. Its main message is that supply shocks are relatively less important than demand shocks, themselves captured by a global index of commodity prices, which became known as “Kilian index”. Due to its impact in the academic literature, several extensions of Kilian (2009) have been developed, usually adding one series in the SVAR to consider the effects of supply and demand not only on real oil prices but also on other financial markets, including U.S. stock prices by Kilian and Park (2009), U.S. bond prices by Kang et al. (2014), exchange rates by Chen et al. (2016a), economic policy uncertainty by Kang et al. (2017) and U.S. consumer sentiment by Güntner and Linsbauer (2018). Another stream of papers has focused on misspecifications of the basic SVAR model to allow for inventories and speculative trading put forward by Kilian and Murphy (2014).

Some authors have examined the causes of why Brent and WTI started to diverge. This is very important because the two oil prices serve as major benchmarks for the world and the U.S. For example, Scheitrum et al. (2018, p. 463) aim to “explain what happened in 2011 when the spread diverged from historical levels, sending WTI to more than a $20 discount under Brent and resulting in the WTI being viewed as a broken benchmark for a time.” Fig. 1a illustrates the divergence between Brent and WTI using monthly data making clear the shift in the spread after 2011 when advances in oil production became evident, as shown in Fig. 1b which compares the world (excluding U.S., right axis), U.S. non-tight and U.S. tight oil production over our sample period. Interestingly, the oil spread started diverging from historical levels at a time when U.S. tight oil production started rising. In this paper we will therefore refer to the first subsample running from 2000 to 2010 and the second running from 2011 to 2018. This paper merges the SVAR literature on real oil prices and the divergence in crude oil prices. Our contribution is not on the causes of the divergence but on the implications of this divergence for stock returns. We believe this is of interest because both academic studies and the financial press have suggested that the vast increase in U.S. oil production may be responsible for the divergence in oil prices, which itself may impact how oil price shocks are transmitted into the financial markets.

This paper examines the pricing of structured finance (SF) – asset-backed securities (ABS), mortgage-backed securities (MBS), and collateralized debt obligations (CDO) – and straight debt finance transactions. Using a cross-section of 24,525 European bonds issued by financial and nonfinancial firms in the 2000-2016 period, we show that although ratings are the most important pricing determinant for SF and corporate bonds (CB) at issuance, investors rely on other contractual, macroeconomic, and firms’ characteristics beyond these ratings. We find that CDO tranches have, on average, higher credit spreads than similarly rated CB, while investors are not compensated for facing higher systematic risk components in relation to investment-grade ABS and MBS. Our results also support the hypothesis of SF transactions as mechanisms of reducing funding costs: SF transactions’ weighted average spread is lower than that of comparable CB and originating firms’ creditworthiness does not deteriorate when compared to a sample of matched firms.

Introduction

Structured Finance (SF), in the form of asset securitization (AS), has become a significant source of financing for a wide variety of assets in recent decades. According to the Securities Industry and Financial Markets Association (SIFMA), the volume of securitized assets in Europe grew from €78.2 billion in 2000 to €818.7 billion in 2008, an increase of 946.9%.1 Despite the important role played by AS in the development and propagation of the 2007-2008 financial turmoil, 2 between 2009 and 2016 a total of €2,290.6 billion of securitized instruments were issued in Europe, compared with €11,732.0 billion in the United States (US). Although financial firms have issued the majority of AS bonds, we show that the issuance of AS bonds by nonfinancial firms located in Europe increased significantly by more than €34.0 billion in 2006, before declining to €1.65 billion in 2010, in the aftermath of the financial crisis (see Appendix A). AS and corporate bond (CB) markets are the largest security markets for corporate debt financing, both in Europe and the US (Choudhry, 2004; Loutskina, 2011). In this paper we compare credit spreads and pricing of AS bonds – asset backed-securities (ABS), mortgage backed-securities (MBS), and collateralized debt obligations (CDO) – with those of CB in a large sample of bonds (9,217 AS and 15,308 CB) issued by European financial and nonfinancial firms between January 1, 2000 and December 31, 2016. We also examine whether credit spreads convey information beyond credit ratings across AS and CB and if AS transactions allow originating firms to reduce funding costs vis-à-vis CB issuances.

This paper examines Single and Dynamic Pricing Policies of two competing firms over two periods in the presence of social influence. Assuming two firms adopt the same pricing policy, we find that, under either pricing policy, firm profits always decrease with the degree of social influence. Firms prefer Dynamic Pricing Policy when social influence is either relatively weak or sufficiently strong (for firms under Dynamic Pricing Policy to set zero prices in the first period). Otherwise, Single Pricing Policy is more preferable. The conclusions are similar when the market size varies over periods, except that Dynamic Pricing Policy is always more profitable if the market size in period 2 is sufficiently large. We have further compared the two pricing policies with Posted Pricing Policy. The results show that Dynamic Pricing Policy dominates when social influence is relatively weak, while Posted Pricing Policy dominates when social influence is sufficiently strong because of the synergy between social influence and the reference price effect. Finally, when each firm freely chooses either Single or Dynamic Pricing Policy, we find that, if the degree of social influence is relatively small, two asymmetric equilibria exist where two firms adopt different pricing policies. If the degree of social influence is very large, however, the unique equilibrium is both firms adopting Dynamic Pricing Policy. These findings provide important implications for firms to make more informed pricing decisions in an increasingly competitive environment with strong social influence.

Introduction

Social influence is defined as “any change which a person’s relations with other people (individual, group, institution or society) produce on his (sic) intellectual activities, emotions or actions” (Dictionary of Personality and Social Psychology (1986, p.328)). It has been found to significantly affect consumer purchase decision. Substantial research has shown that consumers prefer products that are popular among other consumers in the previous selling periods (Hu et al., 2015). This explains why almost all the online shopping websites (e.g., Amazon and Target) update the sales ranking information from time to time to show which product is more popular. Particularly, Amazon adds a “best seller” tag on the product that is very popular. In some websites, they also show the previous sales quantity of each product. For example, the daily deal site, DailyDeal, and eBay both show the sales quantity of their products. Similarly, T-mall and Taobao, the two most famous shopping websites in China, also reveal the sales quantity in the previous month of each product, which will certainly be an important reference for consumers to make purchase decisions. The underlying motivation of displaying the sales quantity information is similar to having “top seller” lists (Parsons et al., 2014). Empirical studies also show that social influence results in the “the rich get richer” phenomenon (Cai et al., 2009; Carare, 2012). Carare (2012) finds that a consumer would like to pay more for a highly ranked product through investigating the sales ranking in Apple’s App Store.

We examine the effect of a pre-IPO audit committee on IPO pricing from the perspectives of information asymmetry and agency problems. We propose a bargaining power hypothesis to disentangle the information asymmetry explanation (financial reporting quality) from the agency problems explanation (underwriter bargaining power) on IPO pricing. IPO underpricing can be reduced by increasing the financial reporting quality under information asymmetry and/or by decreasing the underwriter bargaining power under agency problems. An audit committee can raise the quality of financial reporting and reduce the bargaining power of underwriters. With a pre-IPO market, the IPO markets in Taiwan have little information asymmetry, thus leading to the weak importance of reducing information asymmetry. We show that the establishment of a pre-IPO audit committee improves IPO pricing efficiency by reducing underwriter bargaining power rather than by raising the quality of financial reporting in Taiwan.

Introduction

An initial public offering (IPO) is the process of offering shares of a firm that is transitioning from private to public status by selling stocks to a large number of diversified investors. Although information is disclosed in the IPO prospectus, it is still difficult to value an IPO because no price history is available for firms before going public. Because of the value uncertainty of IPO firms, investors cannot fully identify high-value IPOs from low-value IPOs. Leland and Pyle (1977) argue that investors benefit from knowing the true information of the issuers. However, moral hazard hampers the truthful release of information from issuers to investors. Issuers gain substantial rewards by exaggerating positive information. To overcome the existence of substantial information asymmetry between them and investors, issuers must reveal information and credible signals to the market. The best-known anomaly of IPOs is the underpricing of IPO shares that are listed for the first time. Lowry et al. (2017) indicate that most of the fundamental models of IPO underpricing focus on information asymmetry. Because of the heterogeneity of skills among investors, some investors obtain more information on the issuing firms than do other investors (Rock (1986); Amihud et al. (2003)).

1- Introduction

2- Data

3- The time-varying relation between inflation and consumption

4- The time-varying inflation risk premium

5- Origins of the time-varying inflation risk premium

6- Model

7- Calibration

8- Robustness checks

9- Conclusion

References

Abstract

We show that inflation risk is priced in stock returns and that inflation risk premia in the cross-section and the aggregate market vary over time, even changing sign as in the early 2000s. This time variation is due to both price and quantities of inflation risk changing over time. Using a consumption-based asset pricing model, we argue that inflation risk is priced because inflation predicts real consumption growth. The historical changes in this predictability and in stocks’ inflation betas can account for the size, variability, predictability, and sign reversals in inflation risk premia.

Introduction

We show that inflation risk is priced in stock returns and that both the price and quantities of inflation risk are strongly time-varying, even changing sign over our sample from 1962 to 2014. We argue that inflation is risky because it predicts real consumption growth in a time-varying way. Positive shocks to inflation sometimes contain bad news for future consumption, whereas at other times they contain good news. We provide new empirical evidence, for both the cross-section of stock returns and the aggregate stock market, and develop an equilibrium model to argue that it is the time-variation in the predictive content of inflation that drives the observed time-variation in the price and quantities of inflation risk. To quantify the predictive content of inflation at a given point in time, we define the nominal-real covariance as the slope coefficient in a conditional (that is, rolling) regression of real consumption growth on lagged inflation. We find that this nominal-real covariance is an economically strong and statistically significant predictor of the inflation risk premium in the cross-section of stocks. We measure the quantity of inflation risk of stocks by their conditional inflation beta, that is, the slope coefficient in a rolling regression of returns on inflation shocks. A one standard deviation increase in the nominal-real covariance predicts an increase in annualized expected return of 5.3% for a high-minus-low portfolio constructed by sorting stocks in deciles according to their inflation beta. Given an unconditional average return for the high-minus-low inflation beta portfolio of -4.2%, this increase implies that the inflation risk premium can switch sign.

1- Introduction

2- Literature review

3- Problem formulation

4- The methodology

5- Results and discussions

6- Conclusion

References

Abstract

Now-a-days, with the ever increasing number of vehicles, getting parking space at right place and on time has become an inevitable necessity for all across the globe. In this context, finding an unoccupied parking slot by the interested vehicle owners with least overhead becomes an NP-Hard problem bounded by various constraints. In-advance availability of information regarding parking occupancy plays a major role in hassle free trip optimization for motorists. It also facilitates services-cum-profit management for the parking owners. It further helps in curbing congestion by reducing cruising time and hence, helps in controlling pollution of the smart cities. Thus, accurate and timely information regarding parking occupancy and availability has become the basic need in the evolution of the smart cities. Motivated by these facts, an occupancy-driven machine learning based on-street parking pricing scheme is proposed in this paper. The proposed scheme uses machine learning based approaches to predict occupancy of parking lots, which in turn is used to deduce occupancy driven prices for arriving vehicles. In order to train, test, and compare different machine learning models, on-street parking data of Seattle city has been used. To the best of our knowledge, this is the first time that parking occupancy prediction system is used to generate occupancy based parking prices for on-street parking system of the Seattle city. Results obtained using the proposed occupancy driven machine learning based on-street parking pricing scheme demonstrate its effectiveness over other existing state-of-the-art schemes.

Introduction

In most of the cities around the world, parking is considered to be a big problem because of many reasons, such as increase in population size, number and size of vehicles, limited parking spaces, traffic congestion on roads, locations of parking lots etc. With the rapid increase in the number of vehicles, getting and providing parking slots has become a challenge for the parkers and transport authorities/owners respectively. Usage of private vehicles over public transportation is always individual’s choice due to varying comfort levels, less travel time, and ease of travel etc. With increase in private vehicles, cruising time and congestion increases invariably. Most of the vehicles spent significant time on roads in searching parking spaces instead of commuting. This increases congestion on the roads. Situations get even worse during rush hours and near hot spots. This unnecessary congestion leads to overcrowding of vehicles, increase in carbon emissions, and raises various traffic management problems.

1- Introduction

2- Related work

3- Market model and problem description

4- Optimal pricing mechanism

5- Numerical results

6- Conclusion

References

Abstract

Deep learning has drawn a lot of attention recently. It is successful in a variety of applications from natural language processing to autonomous vehicle control. A main difference from traditional learning is that deep learning learns the representations of the data, i.e., the features, via greedy layer-wise pre-training. The characteristics of deep learning promotes it as a power tool to mine the data from Internet of Vehicles (IoV). The paper focuses on the economic aspect of IoV and investigates deep learning enabled IoV market for data trading and processing. The economic model of IoV consists of three side: the data provider, the service provider, and the user. The data provider collects the data for the user. The user buys the raw data. The data is further processed by the service provider, who provides the learned features for the user to obtain some profit. To optimize the profit of three-sided participators, a Stackelberg game is proposed to model the interactions among them. We derive the equilibrium pricing mechanism of the providers and corresponding demands of the users. The existence and the uniqueness of the equilibrium strategies are proved. Our analysis reveals that the strategy of each participator is related to the utility of the user and the data/service provider’s cost. To the best of our knowledge, this is the first time that the data provider and the service provider directly interact with the user in the data market.

Introduction

With the rapid development of wireless communication, mobile computing, sensor techniques, and mobile social networking, many objects in our daily lives are interconnected through the Internet. The past few years have witnessed the proliferation of Internet of Things (IoT) paradigm [1–5]. Via smartphones, diverse sensors/actuators, Radio Frequency IDentification (RFID) tags, an enormous amount of data from different aspects and sectors is generated everyday. For instance, it is reported Google approximately processes 20,000 TB data and Flicker approximately generates about 3.6 TB data everyday [6]. It is expected that by 2020, the total amount of data worldwide will reach 35 ZB [7]. The services offered by IoT paradigm highly depend on the available data. However, approximately 90% of the generated data is unstructured [8]. The multimedia data collected from the Internet and mobile devices is a typical example of this unstructured data [6]. Fully utilizing the collected data to generate commercial value is a huge challenge in many scenarios [9]. Many data preprocessing and machine learning methods are proposed to extract useful information from the data. Machine learning technology enables many aspects of modern people’s life, including web search, recommendation systems, natural language understanding as well as social networking [10–15]. Machine learning facilitates many services to Internet of Vehicles (IoV) [16]. With the aid of multiple sensors deployed in vehicles and roads, cloud platforms collect traffic information and current status from the vehicles [17]. Via data analysis and prediction, drivers are provided by accurate travel time estimation to improve their driving plans [18].

1- Competition is fierce

2- Pricing power explained

3- What drives pricing power?

4- The impact of pricing power is real

5- Having pricing power is not enough

6- How does a firm operationalize pricing power?

7- What is the firm’s priority?

References

Abstract

In the face of rising business complexity and competitive pressure, many firms succumb to price pressures and enter pricing wars. Others focus on creating, quantifying, and capturing pricing power from their market. Since 2011, with help from Warren Buffet and Jim Cramer, Wall Street financial analysts have begun paying close attention to firms with great pricing power. But what is pricing power? How do firms know whether they have it? And how does it affect profit? This research, based on a survey of 128 organizations, identifies and validates the drivers of pricing power and its impact on firm performance. In this article, I create and validate a pricing power assessment instrument that firms can use to get started. This article offers practical recommendations for go-to-market functions on how to start the pricing power discussion, how to measure it, and how to operationalize it.

Competition is fierce

Cutting prices or putting things on sale is not sustainable business strategy. The other side of it is that you can’t cut enough costs to save your way to prosperity. –—Howard Schultz, CEO of Starbucks (Gossage, 2011) The business world continues to experience dynamic changes. Change is everywhere. Its pace feels exponential, it has tremendously increased competitive pressures, and it has disrupted the world of pricing (see Table 1). Faced with tremendous pricing pressures, many executives have succumbed to competitive intensity by entering price wars, not fully realizing the consequences of such actions (Kramer, Jung, & Burgartz, 2016). A 2016 global pricing study reported that 82% of companies complained of pricing pressures, 49% were experiencing a price war in their industry, and only 30% managed to increase prices successfully in their market (Simon-Kucher & Partners, 2016). While most firms compete on price, others have realized that, after being faced with pricing pressure, years of flat demand, and several waves of cost optimization efforts, they must think differently. They cannot cut their way to prosperity. There is no winning in a price war. Firms have no choice but to invest massively in innovation processes to create value for their customers and markets (Hinterhuber & Liozu, 2012). Wall Street has begun to pay attention to this shift, and financial analysts have started assessing firms’ pricing power (Krishna, Feinberg, & Zhang, 2007). Success stories of pricing power masters have emerged in the financial news (e.g., Apple, Starbucks, Google, Lego, Disney, 3M, Grainger, Netflix, Johnson & Johnson, Caterpillar, Sealed Air). In March 2016, Jim Cramer (Stevenson, 2016) observed: “Sometimes, it just comes down to figuring out who has pricing power: who can raise prices and who can’t.

With the popularity of omnichannel retailing, consumers are sophisticated enough to make strategic channel decisions, considering the possible disappointment for an unexpected outcome induced by various uncertainties from online and offline channels. Value uncertainty online can trigger low-value disappointment, and availability uncertainty offline may cause stock-out disappointment. This study characterizes the effect of consumers’ anticipated disappointment aversion behavior on the optimal pricing decisions of retailers with or without inventory constraint in the omnichannel environment. When a retailer operates in dual-channel and faces offline inventory constraint, the concern for consumers’ homogeneous disappointment aversion changes the threshold above which the retailer implement different channel pricing strategies. Then, we show how the negative impact of disappointment on profit and market can be mitigated by physical showroom mechanism, which is increasingly adopted by omnichannel management. Introduction of a cost-effective physical showroom expands the market by relaxing the restriction of stock-out disappointment and improves profit as long as consumers’ low-value disappointment is high enough. Interestingly, the low-value disappointment benefits the omnichannel in expanding the market coverage by alleviating the constraint exerted by stockout disappointment. Further discussions regarding the disappointment behavior are derived from the extension of consumers’ heterogeneity in disappointment-aversion.

Introduction

With the increasing digitalization in marketing and retailing, the retail landscape continues to change with the integration of new phenomena, such as mobile shopping and social media, in online and offline channels. A new concept called online-tooffline is increasingly becoming popular. As consumers gradually take advantage of the seamless and unified shopping experience offered by the emerging business models, new opportunities and challenges have emerged for retailers (Bell et al., 2014; Harsha et al., 2016). Based on existing online and offline channel management, an increasing number of companies are transforming into omnichannel operations, a new retail mode that emphasizes the interplay between channels and consumers. For instance, Wal-Mart, the worlds largest retailer and a model brick-and-mortar store, has explored its business online with new strategies. Wal-Mart offers a new service through which customers can order goods online and pay for them at a nearby store with cash. New features of the Wal-Mart App, such as savings catcher (a new price-check function) and Geo-fence, offer a convenient and interactive shopping experience. The solely e-commerce store of the New York-based eyeglasses brand Warby Parker has supplemented its operation with the establishment of stores for product display and brand promotion. Even dual-channel retailers have explored the supply and demand interactions from crossing channels and integrating price and inventory-sharing decisions to promote omnichannel efficiency and performance. Therefore, to thrive in this new environment, retailers of all types should take a broader perspective on channels and reexamine their strategies for delivering information and products to consumers, who move among channels in their search and buying process (Bell et al., 2014; Verhoef et al., 2015).

This study examines the impact of accounting comparability on financial reporting quality and the extent to which financial statement users understand the implications of firms’ accruals. We predict that comparability improves the information environment, which not only enhances the ability of managers to estimate accruals more accurately and signal their private information, but also improves investors’ comprehension of accruals. Utilizing restatements, the mapping of accruals into cash flows, earnings persistence, and audit fees as measures of financial reporting quality, we find that prior-period comparability is associated with higher financial reporting quality. We also provide evidence that comparability is positively associated with managerial forecast accuracy and precision, consistent with comparability improving the ability of managers to predict future firm performance. Furthermore, we find that when prior-period comparability is higher, current period discretionary accruals are less positively correlated with contemporaneous returns and less negatively correlated with future returns, consistent with our prediction that comparability improves the pricing efficiency of accruals. Our results are robust to controlling for the endogeneity of accounting comparability and several different empirical model specifications. Overall, our findings suggest that enhanced accounting comparability is beneficial to both preparers and users of financial statements.

Introduction

Regulators argue that comparability increases accounting information’s usefulness and enables financial statement users (hereafter users) to identify the similarities and differences between economic phenomena (Financial Accounting Standards Board (FASB), 2010). The Financial Accounting Standards Board (FASB) (1980) states that “investing and lending decisions…cannot be made rationally if comparative information is not available.” Financial statement analysis textbooks also stress the importance of comparability in judging a firm’s performance (e.g., Revsine, Collins, Johnson, & Mittelstaedt, 2011). Existing research on accounting comparability primarily focuses on its benefits to financial statement users such as financial analysts and creditors (e.g., De Franco, Kothari, & Verdi, 2011; Kim, Kraft, & Ryan, 2013). Very few studies have examined the implications of comparability for managers, an important party in the information production and dissemination process. Also, the implications of comparability for users have not been fully explored. To fill these gaps in the literature, we examine the impact of comparability on the quality of managers’ financial reporting and the extent to which investors understand the implications of firms’ accruals, given the impact of comparability on financial reporting quality.1 Following the Financial Accounting Standards Board (FASB) (2010) and De Franco et al. (2011), we define comparability as the extent to which similar economic transactions are accounted for similarly, and dissimilar transactions are accounted for differently.

This study investigates the strategic effect of return policies in a dual-channel supply chain, in which a manufacturer can sell products directly to end customers and indirectly via an independent retailer. The manufacturer decides whether to implement a return policy in either the direct or indirect channel, or in both channels. When the return policy is provided, the cost of returned products has to be covered by the corresponding channel. We consider four possible strategies, including full refund in the direct channel only, full refund in the indirect channel only, full refund in both channels, and no refund in both channels. Given the equilibrium pricing strategies of firms, the preferences of firms over different strategies are determined by comparisons between the anticipated return rate of customers and their perceived value of the return policy. The manufacturer prefers the full-refund policy in both channels when the return rate of the customer is low. Otherwise, the dominant strategy for the manufacturer is the no-refund policy in both channels. The retailer benefits more from the full-refund policy in the indirect channel alone when the return rate is low; otherwise, the retailer prefers the full-refund policy in the direct channel only.

Introduction

Customers in numerous industries have a legal right to return their purchased products to original manufacturers or retailers for any reason (Mostard and Teunter, 2006). In 2007, the U.S. electronics industry spent approximately $13.8 billion on returned products, in which only approximately 5% were defective (Lawton, 2008). According to Jim Brill, the reverse logistics marketing manager for UPS, the expense of returns processing ranges from 20% to 65% of the cost of goods sold1 . This phenomenon is even more prevalent in online shopping because e-commerce products are usually characterized by substantial numbers and high return rates (Choi et al., 2004; Disney et al., 2004; Cho et al., 2008; Yang et al., 2015). Based on an Invesp infographic of online return rates, online shoppers return at least 30% of their purchases, whereas in physical stores less than 9% return their purchases2 . The preceding discussion explicitly shows that product return is no longer a small part of the business process. Numerous scholars (Chen and Bell, 2011; Chen and Grewal, 2013; Chen and Bell, 2013) also indicate that offering a return experience should be viewed as an important weapon for a firm to build a competitive advantage in the marketplace. Although a substantial body of literature discusses the manner in which return policies affect the profits of channel members (Chen and Bell, 2011; Chen and Bell, 2013), most studies focus on a traditional supply chain setting where manufacturers sell products via retailers exclusively. Nonetheless, an increasing number of manufacturers now have an attempt to sell to customers via retailers and e-commerce. For example, Dell, which is known for its success in the direct-selling mode, announced that it would build an indirect selling channel to take the advantage of both channels and to stimulate sales.

This study investigates the optimal level of transfer prices chosen by managers in a divisionalized firm when they are evaluated based on a balanced scorecard. A unique assumption of our model is that transfer prices are unobservable to a competing firm’s managers. In contrast to the findings in several studies that examine strategic transfer pricing, this research shows that a manager who is evaluated using a balanced scorecard chooses a transfer price that exceeds marginal cost given a market competitor in a specific economic environment. This result is caused mainly by our model’s assumption that a manager considers the competitor’s profit in his/her in decision-making when the objective is to maximize long-term profit. This study makes a significant contribution to the strategic transfer pricing literature by showing that even if the transfer price is unobservable to rivals, the optimal transfer price exceeds marginal cost when the final product market is characterized by price competition, something not shown in previous analytical accounting research.

Introduction

Transfer prices have become a critically important issue for multidivisional firms. For example, General Motors and Panasonic use effective transfer prices to optimize their divisional operations and profits based on the principle that transfer prices allow managers to evaluate a manufacturing division’s performance as a profit center. While multidivisional firms attempt to effectively and accurately evaluate divisional performance using transfer pricing, it is often difficult to determine the optimal transfer price in a firm composed of multiple divisions. Because firms are generally interested in determining the optimal transfer price in a specific economic environment, transfer pricing is a practical and important issue in contemporary management accounting. Further, methods of optimal transfer pricing have been explored and proposed in empirical research from a management accounting perspective because selecting the optimal transfer price can improve a firm’s profit (e.g., Chan & Lo, 2004; Tang, 1992). Economic analysis of transfer pricing from a managerial viewpoint dates back to Hirshleifer (1956), who advocates that an internal transfer price equal to marginal cost alleviates any attendant double marginalization problem. Since Hirshleifer’s (1956) work, other management accounting studies have analyzed optimal transfer prices using market competition models (e.g., Alles & Datar, 1998; Arya & Mittendorf, 2007; Autrey & Bova, 2012; Fjell & Foros, 2008; Göx, 2000; Hamamura, 2018; Matsui, 2011, 2012, 2013; Narayanan & Smith, 2000; Schjelderup & Sørgard, 1997; Shor & Chen, 2009). Most studies in this research stream examine which cost-based transfer price, other than marginal cost, is desirable for a firm (e.g., Alles & Datar, 1998; Matsui, 2011, 2013). Tang (1992) provides important empirical evidence on transfer pricing practices, specifically the relationship between transfer prices and the use of a cost accounting system. Tang (1992) describes only a few situations where firms choose marginal cost to set transfer prices, which is unlike the theoretical insight gained in Hirshleifer (1956).