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The purpose of this paper is to demonstrate the application of the control chart procedure to monitor the characteristics whose profile over time resembles a set of connected line segments.

Fit a regression spline model by taking the subgroup average of the characteristic as response variable and time as the explanatory variable. Then monitor the response variable using the regression spline control chart with the fitted model as center line and upper and lower control limits at three standard deviation units of the response variable above and below the center line.

The proposed chart is successfully deployed to monitor the daily response time profile of a client server of an application support process. The chart ensured the stability of the process as well as detected the assignable cause leading to the slowing down of the server performance.

The methodology can be used to monitor any characteristics whose performance profile over time resembles a set of connected line segments. Some of the examples are the consumption profile of utility providers like power distribution companies, usage profiles of telecom networks, loading profile of airline check-in process, e-commerce websites, etc.

To the best of the author’s knowledge, construction of control charts using regression spline is new. The usage of the control chart to monitor the performance characteristics which exhibits a nonlinear profile over time is also rare.

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods, and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader, and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.

On the basis of corporate wholesale and hypermarket stores, this study aims to investigate the relationship between energy consumption, physical building characteristics and operational sales performance and the impact of energy management on the corporate environmental performance.

A very unique dataset of METRO GROUP over 19 European countries is analyzed in a sophisticated econometric approach for the timeframe from January 2011 until December 2014. Multiple regression models are applied for the panel, to explain the electricity consumption of the corporate assets on a monthly basis and the total energy consumption on an annual basis. Using Generalized Additive Models, to model nonlinear covariate effects, the authors decompose the response variables into the implicit contribution of building characteristics, operational sales performance and energy management attributes, under control of the outdoor weather conditions and spatial–temporal effects.

METRO GROUP’s wholesale and hypermarket stores prove significant reductions in electricity and total energy consumption over the analyzed timeframe. Due to the implemented energy consumption and carbon emission reduction targets, the influence of the energy management measures, such as the identification of stores associated with the lowest energy performance, was found to contribute toward a more efficient corporate environmental performance.

In the context of corporate responsibility/sustainability of wholesale, hypermarket and retail corporations, the energy efficiency and reduction of carbon emissions from corporates’ real estate assets is of emerging interest. Besides the insights about the energy efficiency of corporate real estate assets, the role of the energy management, contributing to a more efficient corporate environmental performance, is not yet investigated for a large European wholesale and hypermarket portfolio.

Many attempts to justify the business value of increased investments in information technology (IT) have shown mixed results. While findings from earlier studies have been conflicting, recent firm level studies indicate that IT investments have a positive impact on productivity. However, whether IT adds value to organizations is an on going debating issue. Thus, thus it is worth of further investigation.

The paper employs multiple techniques – a regression, regression trees, and regression splines – and integrate the responses provided from each technique.

While IT investments have a positive impact on productivity, the impact is conditional and is not uniform but depends on the amounts invested in other related areas, such as non‐IT labor, non‐IT capital, and/or IT investments.

The IT impact on productivity can be maximized when investments in other related areas are considered together than when they are considered in isolation. Therefore, IT investment decisions should not be made without consideration of the levels of other investments within an organization to avoid any waste in additional investments in IT.

While most previous studies have studied in terms of its existence or non‐existence of the IT impact, we investigate the conditions under which the IT impact would or would not exist. Thus, our study provides with the opportunity for gaining a deeper understanding of the impact of IT investments on productivity.

The paper aims to examine the impact of information technology (IT) on the financial performance of third‐party logistics (3PL) firms in China.

A questionnaire‐based mail survey was conducted in mainland China. Path analysis and spline regressions were used to model the relationship between IT and financial performance.

This study found that IT can significantly improve 3PL firms’ financial performance and there are complementarity and plateau effects of influencing financial performance between IT advantage and executives’ involvement in both IT and business strategy planning.

The modeled relationship between IT and competitive advantages may not hold in different cultural environments and industrial settings. The sample size was small. Perceptual performance data were used.

To achieve better financial performance, it is essential for 3PL firms to sense strategically the importance of IT and commit sufficient managerial efforts and resources to achieve IT competency. In addition, IT executives should involve themselves in strategic business planning to better understand business strategies.

The results of this study – which constitutes the first to investigate IT in the 3PL industry in China – provide empirical evidence and a better description of the relationship between IT and financial performance. The findings provide valuable managerial guidance and insights for logistics managers.

We consider a semiparametric panel stochastic frontier model where one-sided firm effects representing inefficiencies are correlated with the regressors. A form of the Chamberlain-Mundlak device is used to relate the logarithm of the effects to the regressors resulting in a lognormal distribution for the effects. The function describing the technology is modeled nonparametrically using penalized splines. Both Bayesian and non-Bayesian approaches to estimation are considered, with an emphasis on Bayesian estimation. A Monte Carlo experiment is used to investigate the consequences of ignoring correlation between the effects and the regressors, and choosing the wrong functional form for the technology.

For over three decades, vector autoregressions have played a central role in empirical macroeconomics. These models are general, can capture sophisticated dynamic behavior, and can be extended to include features such as structural instability, time-varying parameters, dynamic factors, threshold-crossing behavior, and discrete outcomes. Building upon growing evidence that the assumption of linearity may be undesirable in modeling certain macroeconomic relationships, this article seeks to add to recent advances in VAR modeling by proposing a nonparametric dynamic model for multivariate time series. In this model, the problems of modeling and estimation are approached from a hierarchical Bayesian perspective. The article considers the issues of identification, estimation, and model comparison, enabling nonparametric VAR (or NPVAR) models to be fit efficiently by Markov chain Monte Carlo (MCMC) algorithms and compared to parametric and semiparametric alternatives by marginal likelihoods and Bayes factors. Among other benefits, the methodology allows for a more careful study of structural instability while guarding against the possibility of unaccounted nonlinearity in otherwise stable economic relationships. Extensions of the proposed nonparametric model to settings with heteroskedasticity and other important modeling features are also considered. The techniques are employed to study the postwar U.S. economy, confirming the presence of distinct volatility regimes and supporting the contention that certain nonlinear relationships in the data can remain undetected by standard models.

This paper analyses the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.

This paper seeks to examine the potential for simplified market value and non market value based banded approaches to be utilised for residential property tax purposes. The broad aim is to ascertain whether relatively low complexity approaches to establishing a property tax base can perform adequately in comparison to established best practice – in essence whether there is evidence of equifinality (equivalent performance from approaches of substantially different complexity) between simpler and more complex approaches.

The research comprises empirical analysis of a database of property sales and property attribute data drawn from a UK District Council area. Several simplified methods are used to create different tax base scenarios and the outflowing tax incidence is compared with that of using a complex, industry standard market value approach. The methods of comparison are regression and spline regression based models testing for tax inequity, drawn from the literature. The approach here differs from previous work in that it occurs at the actual tax bill level allowing the comparison of value, non‐value and banded approaches.

The findings of the research indicate that simplified approaches to establishing a property tax base can perform in a broadly similar fashion to more complex systems currently practiced in developed economies and therefore evidence of equifinality exists.

The research provides useful tools to property tax policy makers and practitioners in developing and transitional economies in furthering their aspirations of embedding robust property taxes for the furtherance of socio‐economic and political development and the general wellbeing of society and they are of value to property tax policy makers and to academics in the field.

The paper provides evidence of the efficacy of simplified and banded approaches as an option for jurisdictions in developing and transitional economic circumstances or elsewhere in circumstances which mitigate against full scale appraisal of the property tax base to discrete market values. The approaches and techniques pioneered open up opportunities to carry out a range of new comparative analysis of tax base options.

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.