Search results

This paper aims to examine how customer heterogeneity influences absolute price thresholds in a service industry.

Customer purchase behavior is studied in the context of a firm’s and competitor’s price changes. Customer purchase behavior is further examined in the context of specific customer attributes such as loyalty, motivation, online purchase channel, gender and frequency of purchase. The study uses a longitudinal data set spanning over 44 months and tracks over 13,000 of a firm’s customers, totaling over 200,000 transactions from a parking services provider.

Results show that absolute price thresholds affect purchasing decisions. Customers are willing to pay a range of prices for a considered purchase, and when a price is within customers’ acceptable price range, it does not induce a change in their purchase behavior. However, specific identifiable customer attributes influence the propensity to continue buying and influence the acceptable price range.

Knowledge from this study can be applied to developing a deeper understanding of customers and their price thresholds to improve customer retention and firm performance after a price change.

For a better understanding of the consumer choice process, it is essential to understand what factors affect price thresholds. Additionally, very few studies are using transaction-level data to empirically validate concepts from behavioral price research in the service industry, and none that do it at an individual customer level over three years.

Sales and market share of store brands have been growing significantly at the expense of national brands. The decision to purchase a store brand or a national brand has been modeled in this paper. The proposed model provides an explanation for the existence of asymmetric price competition between store brands and national brands. The article proposes and empirically demonstrates the existence of a reference threshold as the key criterion underlying this choice. It also shows that the decision to buy the store/national brand is not influenced by the store brand’s price or price promotions, or the magnitude of the difference between the threshold and the national brand’s price.

The purpose of this paper is to introduce a new approach for the identification of price thresholds, which enables learning true thresholds from previous buying decisions recorded in POS scanner data.

The methodology presented herein combines spline regression with generalized cross‐validation. Classical Chi‐square testing confirms the separation of regimes of the price response function by this methodology. Five propositions concerning the consumers' response to odd pricing in a Western‐type market are evaluated.

Despite the widespread retail practice odd prices are unlikely to flag the actual threshold in consumer response. The term odd price refers to prices with a non‐zero ending in the cent digit, e.g. .95, .98 or .99, which are commonly used in Western‐type markets. Moreover, the simple odd price effects are distinguished from odd‐ending prices with the first number left of the decimal point set to an odd number. The results show that even these prices not always flag a threshold in consumer response.

The discussion of the odd‐price effect is confused by conflicting empirical results and related interpretations of the underlying mechanisms. In contrast to many previous investigations – which are restricted to the consideration of very few price endings – this study covers all reasonable prices. Statistically significant odd‐price effects are found for some brands, but not for all within the same category.

One must argue for checking the thresholds for each brand individually rather than generalizing by applying misleading rules of thumb.

The paper provides researchers as well as practitioners with a methodology to evaluate price thresholds and outlines the shortcoming of contemporary retailers pricing practices in a detailed manner.

The purpose of this study is to analyze market inefficiencies in the market for cryptocurrencies by providing a comprehensive analysis of short-term (over)reactions that follow significant price changes of such currencies.

This study identifies and analyzes overreactions and mispricing in markets for cryptocurrencies by applying a broad set of thresholds that depend on market-specific dynamics and volatilities. This study also analyzes the returns on days following abnormal returns and identifies significant differences from normal returns using the t-test and the Mann–Whitney U-test. The researchers further complement the literature by using end-of-the-day returns in addition to high-low returns. Additionally, this study considers a broad sample of 50 cryptocurrencies for an expanded time span (2015–2020) that includes the big currencies as well as smaller currencies.

Findings detect the existence of overreactions and, thus, market inefficiencies in crypto markets. The findings for different methodological approaches are similar, which underpins the robustness of the findings. By considering a broad sample that includes small and big currencies, we can show the existence of a market size effect. By considering a broad set of thresholds, the authors further found evidence for a magnitude effect, which means that higher initial abnormal returns are related to higher inefficiencies.

This paper has practical implications. Market inefficiencies were detected, which can be used in practical trading to obtain excess returns. In fact, methodological approach of this study and its results can be used to derive a strategy for trading in cryptocurrencies that can be easily implemented. Based on the study’s findings, the authors can expect positive access returns by applying this trading strategy.

The authors complement the literature on market inefficiencies and mispricing in crypto markets by analyzing price patterns after initial abnormal returns. Researchers contribute by applying different methodological approaches in addition to the approaches used so far, by considering a set of different thresholds and by applying a much broader data set that enables the study to analyze additional aspects.

Wheat, which is one of the major staple food grain crops in India, continues to depict occasional fluctuation in the prices though Union government has adopted administered price policy for wheat by intervening in its procurement at assured prices and distribution. Such fluctuations in prices are usually attributed to inefficient functioning of the agricultural markets. Since spatially separated markets also play an important role to determine efficiency of the agricultural markets, the study has used market integration as one of the tools to analyze the price transmission across the spatially separated markets to identify causes of price fluctuations and suggest ways to stabilize wheat prices.

The study utilizes monthly wholesale prices for January, 2006 to May, 2016 for dara wheat. First, the study employs augmented Dickey and Fuller (ADF), Phillips and Perron (PP) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests to check stationarity in wheat prices. Second, Johansen's cointegration test is applied to assess the integration of wholesale prices between selected pairs of wheat markets to determine long-run relationship among them. Third, Granger casualty test is used to find the direction of causality between the wheat market pairs. Finally, threshold vector error correction model (TVECM) and likelihood ratio (LR) tests are employed to examine long-run adjustment of prices towards the equilibrium in selected wheat markets.

Since wheat wholesale prices for the selected markets are found to be integrated of the order one, that is [I(1)], Johansen's test of cointegration is employed and its findings reveal that the selected wheat market pairs exhibit cointegration and show a long-run price association among themselves. There exists a bi-directional causality among the wheat market pairs. Since LR test is in favor of threshold model (except for Etawah–Delhi pair), one and two threshold models were also performed accordingly. Findings show that wholesale prices of wheat in Delhi markets remain higher than the prices of all other regional markets as regional markets are found to adjust their prices towards Delhi market. Distance of the wheat markets from each other is directly associated with threshold parameters, which are analogous to the transaction costs. Geographically dispersed wheat markets incorporate high transaction and vice versa.

The study argues that there is need to improve rural infrastructure and connectivity of the agricultural markets and remove market asymmetries through unified market regulating mechanisms across the states. This will enable price adjustment process from primary wholesale markets (in production regions) to the secondary wholesale markets (in scarcity regions) quickly.

The contribution of the study in the existing literature lies in the fact that there are no empirical evidences in the context of India that use price transmission as a tool of market integration among spatially separated wheat markets using TVCEM as this model examines role of transaction costs in efficient functioning of the agricultural markets.

The study investigates the impact of inflation on the variability of relative prices in the context of Lebanon.

Unlike the traditional method, which relies on the variance of cross-sectional price changes measured at specific points in time to gauge the variability in relative prices, we employ a more appropriate approach. Under this approach, we capture the dispersion in relative prices by estimating how widely (or closely) a set of commodity prices drift apart over a span of time, offering a more comprehensive assessment. Firstly, we employ Johansen’s cointegration test on rolling subsamples to determine the number of statistically significant cointegrating vectors among the prices of 12 major commodity groups. Under this approach, an increase in the number of significant cointegrating vectors indicates a reduction in relative price variability, while a decrease suggests the opposite. Subsequently, we employ ordinary least squares regression to analyze how the fluctuations in inflation affect the variability in relative prices. The sample period ranges from December 2007 to April 2021.

The empirical results indicate that there exists a certain threshold inflation rate corresponding to which the variability in relative prices is minimized. More importantly, consistent with the theoretical predictions, the results suggest that it is not inflation per se, but the deviation of inflation from the 3% threshold level in either direction that causes higher dispersion in relative prices.

The empirical findings from this study have crucial implications for the operation of monetary and fiscal policy. In particular, these findings suggest that stabilizing long-term inflation around a certain threshold rate will not only help to anchor inflation expectations effectively but will also minimize the welfare costs associated with inflation.

Given the rising inflationary pressure in the recent past and its welfare costs, the study assumes crucial importance in understating how fluctuations in inflation distort the relative price structure and eventually cause resource misallocations and economic inefficiency.

Using information systems via data mining and cluster analysis technologies, consumers' strategic behaviour can be measured, and their patience levels can be accurately described. This paper investigates the retailer's pricing and ordering policies when facing strategic consumers with different levels of patience and discusses the impacts of consumers' patience levels and proportions on retailers' maximum expected profits.

By cluster analysing transaction data on the number of websites visited, browsing time and purchase decision time, consumers' patience levels can be obtained. The authors formulate a newsvendor model considering customers' different patience levels. Three scenarios are investigated: two segments of consumers with two different levels of patience (Scenario I), multiple segments of consumers with different levels of patience (Scenario II) and a continuum of consumers whose levels of patience follow a continuous distribution (Scenario III). Then, general formulas are deduced for retailers' optimal prices, ordering quantities and profits.

Under Scenario I, if the proportion of less patient consumers is greater (less) than a threshold, the retailer's optimal price is equal to the less (more) patient consumers' reserve price. Under Scenario II, once the proportion of fully strategic consumers exceeds a certain threshold, the retailers' optimal price is equal to the fully strategic consumers' reserve price regardless of consumers' patience levels and proportions. Under Scenario III, the retailer's pricing and ordering policies depend on the distribution of their patience level.

Few studies have considered consumers' different levels of patience when making retailer pricing and ordering decisions. In this paper, strategic consumer behaviour is measured, and consumers' patience levels and proportions are obtained by cluster analysing consumer transaction data recorded by an information system. Three scenarios in which strategic consumers may be heterogeneous and have different patience levels are investigated. The results can guide retailer decision-making.

The purpose of this research is to test for price threshold effects in the demand for high-involvement services for small businesses.

The authors use a stated preference choice-based conjoint study of small business telecommunications demand. Using survey data, individual-level parameter estimates for a demand model are achieved via the Hierarchical Bayes method of estimation.

For demand for small business telecommunications services, the authors find very strong positive impacts of nine-ending and zero-ending prices on the demand for a common bundle of telecommunications services (wired telephone service, broadband internet, and cellular telephone service), even at prices so high a shift in the left-most digit does not occur.

The advertising, brand, or product manager or statistician who assumes threshold effects are not extant in high-involvement service demand may find conventional demand estimation methods lead to erroneous conclusions and less effective pricing strategies.

In the statistical literature on price-ending effects on product demand, most products for which demand is modelled are low-involvement consumer products priced at less than ten monetary units per unit of product. There is a lacuna in this price-ending effects literature regarding small businesses and high-involvement services offered at three-digit prices via monthly subscription. This research indicates that testing for threshold effects should be de rigeur in the methodology of demand estimation for telecommunications or other high-involvement services.

The purpose of this paper is to assess price linkages and patterns of transmission among producer and consumer markets for apple in Slovenia.

Non-linear error correction models are applied. Non-linearities are allowed by means of threshold and multivariate local linear regression estimation techniques. Monthly prices over the period 2000-2011 are used in the empirical application.

Both techniques provide evidence of non-linearities in price adjustments. Findings suggest that producer and consumer prices tend to increase rather than decrease. Results also indicate that parametric threshold approaches may have difficulties in adequately representing price behavior dynamics.

The main contribution of this work to the literature relies on the fact that this is the first attempt to assess vertical price transmission in the apple sector in Central and Eastern European Country markets. Further, it is the first attempt to use multivariate local linear regression techniques in this context.

The chapter studies strategic default using an experimental approach.

The experiment considers a stochastic asset process and a loan with no down-payment. The treatments are two asset volatilities (high and low) and the absence and presence of social interactions via a direct effect on the subject's payoff.

I demonstrate that (i) people appear to follow the prediction of the strategic default model quite closely in the high asset volatility treatment, and that (ii) incorporating social interactions delays the strategic default beyond what is considered optimal.

The study tests adequately the strategic default using a novel experimental design and analyzes the neighbor's effect on that decision.