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This paper aims to test the similarity of the results of on-screen eye tracking compared to mobile eye tracking in the context of first fixation location on stimuli.

Three studies were conducted altogether with 117 participants, where the authors compared both methods: stationary eye tracking (Tobii Pro X2-60) and mobile eye tracking (Tobii Pro Glasses 2).

The studies revealed that the reported average first fixation locations from stationary and mobile eye tracking are different. Stationary eye tracking is more affected by a centre fixation bias. Based on the research, it can be concluded that stationary eye tracking is not always suitable for studying consumer perception and behaviour because of the centre viewing bias.

When interpreting the results, researchers should take into account that stationary eye tracking results are affected by a centre fixation bias. Previous stationary eye tracking research should be interpreted with the centre fixation bias in mind. Some of this previous work should be retested using mobile eye tracking. If possible small-scale pilot studies should be included in papers to show that the more appropriate method, less affected by attention biases, was chosen.

Managers should trust research where the ability of package design to attract attention on a shelf is tested using mobile eye tracking. The authors suggest using mobile eye tracking to optimise store shelf planograms, point-of-purchase materials, and shelf layouts. In package design, interpretations of research using stationary eye tracking should consider its centre fixation bias. Managers should also be cautious when interpreting previous stationary eye tracking research (both applied and scientific), knowing that stationary eye tracking is more prone to a centre fixation bias.

While eye tracking research has become more and more popular as a marketing research method, the limitations of the method have not been fully understood by the field. This paper shows that the chosen eye tracking method can influence the results. No such comparative paper about mobile and stationary eye tracking research has been done in the marketing field.

This study aims to apply unit root test to investigate the behavior of Chinese firms toward their leverage policy. The study is based on two influential and competing theories of capital structure.

This study applies unit root test to investigate the behavior of Chinese firms toward their leverage policy. The study is based on two influential and competing theories of capital structure. Trade off theory advocates that firms have a target level of leverage ratio and that firms try to achieve that optimal leverage ratio, whereas pecking order theory argues that firms have no target level of leverage and that they follow a specific pattern of leverage. For this purpose, this study applies a Fisher type unit root test to 12,808 firm level observations. The data are unbalanced and cover a period from 1991 to 2014.

The results reveal the presence of a stationary behavior across short-term, long-term and total leverage policies. For short-term leverage policy, 21 per cent firms show stationary behavior, while for long-term, 20 per cent show a targeting behavior; for the total leverage policy 17 per cent of firms are found to follow a tradeoff model. To make the findings more interesting sample was further classified into profit and loss making firms. The study finds that loss making firms do not follow a target level of leverage in China. Furthermore, unit root is applied to all firms before and after crises-2008. It is revealed that stationary behavior is more prevalent before crises-2008.

This study is highly important from the point of view that it quantifies firms into distinct categories of following specific model of capital structure. To the best of the author’s knowledge, the findings of this study add to current research knowledge about Chinese firms with respect to adjustment behavior toward a target capital structure.

In financial analysis, forecasting often involves regressing one time series variable on another. However, to ensure that the models are correctly specified, one needs to first test for stationarity, co‐integration and causality. In testing for causality, the variables should be stationary. If non‐stationary, one can estimate the model in difference form, unless the variables are co‐integrated. This article determines whether cash flow and earnings variables are stationary, and which variable causes the other, using econometric analysis. In most cases, cash flow variables are found to cause earnings variables. This is so when the models are estimated in levels. However, when estimated in first differences, the causal relationship tends to be reversed such that earnings cause cash flows. Further study is recommended, whereby panel data could be used to improve the power of the tests.

The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM_2.5 through wavelet and regression analysis.

In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM_2.5 as a dependent variable.

It is found that few pollutants such as NO₂, NO_x, SO₂, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM_2.5. The regression model developed has high R² value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model.

The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM_2.5.

Control measures such as control charts can be implemented for significant factors.

Rules and regulations can be made more stringent on the factors.

The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

This paper investigates whether mean reversion holds for a panel of 16 OECD stock price indices for the period 1970 to 2011.

We employ seemingly unrelated regression (SUR)-based linear and non-linear unit root tests which are not only able to exploit the power of panel data analysis but also account for cross sectional dependencies as well as identify which panel members are stationary.

In contrast to a literature that offers mixed findings on stationarity, it was found that most of our sample is characterized as mean- or trend-reverting with approximated half-lives in the region of three to five years.

In contrast to other panel unit root tests of stock prices, the authors identify which individual panel members are stationary and non-stationary using a SURADF test. A further novelty of our approach is that we also develop a SUR-based panel KSS test that allows us to explore the possibility that stock prices exhibit non-linear stationarity.

The purpose of this paper is to consider whether or not the introduction of inflation targeting (IT) impacts on the mean-reversion properties of inflation and output growth.

Focusing on eight Asian countries of which four are inflation-targeters, the authors employ a two-state Markov-switching model which characterizes the behavior of inflation and output growth as regime-dependent based on periods of stationarity or non-stationarity.

In contrast to a literature that offers mixed findings, the authors find the presence of stationary inflation and output growth in one regime for all IT countries, except for South Korea which is characterized by stationary output growth in both regimes. In the cases of South Korea and Thailand, IT reduces the probability of inflation remaining in a non-stationary regime. IT increases the probability of South Korea remaining in a regime of low persistence output growth. While IT is important in understanding behavior, so are other considerations such as exchange rate volatility, as well as the Asian and global financial crises.

In contrast to other unit root tests of inflation and output growth, a novelty of the approach is that the authors obtain new insights in terms of two concepts of stationarity that allow for inflation and output growth to switch between stationary and non-stationary regimes (partial stationarity), or between stationary regimes of differing degrees of persistence (varied stationarity).

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

Following Clarida and Taylor, the term structure of forward exchange premiums can be interpreted as multiple cointegration vectors, if it is assumed that departures from the risk‐neutral efficient markets hypothesis are stationary. This hypothesis is tested using spot rates and one‐month and three‐month forward rates for six European countries during the 1920s floating rate era. Beginning in late 1924, speculation about a return to gold may have resulted in a non‐stationary forward premium. However, except for this speculative period, the term structure of forward premiums was stationary for three currencies. Thus the empirical results presented are broadly consistent with the analysis of Taylor and McMahon, MacDonald and Taylor and Miller and Sutherland.

The development of information technology has made it possible for companies to get access to information about their customers ' future demand. This paper outlines various approaches to utilize this kind of visibility when managing inventories of end products on an operative level. The purpose is to explain the consequences, for capital tied up in inventory, of sharing four different types of planning information (point-of-sales data, customer forecasts, stock-on-hand data, planned orders) when using re-order point (R,Q) inventory control methods in a distribution network.

A simulation study based on randomly generated demand data with a compound Poisson type of distribution is conducted.

The results show that the value of information sharing in operative inventory control varies widely depending on the type of information shared, and depending on whether the demand is stationary or not. Significantly higher value is achieved if the most appropriate types of information sharing are used, while other types of information sharing rather contribute to decreased value. Sharing stock-on-hand information is valuable with stationary demand. Customer forecast and planned order information are valuable with non-stationary demand. The value of information sharing increases when having fewer customers, and when the order quantities are large. Sharing point-of-sales data is not valuable, regardless of the demand type.

The use of simulation methodology is a limitation, because the study has to be limited to a specific model design, and because it is not based on primary empirical data. The study is especially limited to dyadic relationships in supply chains, and to distribution networks with a rather limited number of customers.

Guidance is given about what type of information should be appropriate to share when different types of demand patterns and distribution networks, and how order batch sizes and lead times affect the value of information sharing when using re-order point (R,Q) methods.

Very limited research providing specific assessments of potential inventory control consequences when sharing planning information in various contexts has been found in the literature. The findings and conclusions also question some previous research on information sharing.

The purpose of this paper is to gain a better understanding of the flow field structure around the race car in two cases: stationary wheel and rotating wheel. In addition, this paper also illustrates and clarifies the influence of wheel rotation on the aerodynamic characteristics around the race car.

The author uses steady Reynolds-Averaged Navier-Stokes (RANS) equations with the Realizable k-ε model to study model open-wheel race car. Two cases are considered, a rotating wheel and stationary wheel.

The results obtained from the study are presented graphically, pressure, velocity distribution, the flow field structure, lift coefficient (C_l) and drag coefficient (C_d) for two cases and the significant influence of rotating case on flow field structure around wheel and aerodynamic characteristics of race car. The decreases in C_d and C_l values in the rotating case for the race car are 16.83 and 13.25 per cent, respectively, when compared to the stationary case.

Understanding the flow field structures and aerodynamic characteristics around the race car in two cases by the steady RANS equations with the Realizable k-ε turbulence model.