Over the last decade, the investigation of economic research questions by use of well-established neurological and neurophysiological methods such as fMRI, EEG, electrodermal activity (EDA) or eye-tracking has led to the new interdisciplinary research field called “neuroeconomics” (e.g., McClure et al., 2004; Bechara et al., 2005; Camerer et al., 2005; Kenning and Plassmann, 2005; Singer and Fehr, 2005; Brosch and Sander, 2013). In this field, the underlying neurophysiological processes of economic decision making have been increasingly elaborated with diverse research foci.

Different research studies focus on the neural correlates of social dimensions in economic markets (e.g., Fehr et al., 2005; Ruff et al., 2013), explore behavioral game theories through a new neurophysiological perspective (e.g., Sanfey et al., 2003; Bhatt and Camerer, 2005) and investigate brain activities related to investors' financial decision-making behavior (e.g., McClure et al., 2004; Kuhnen and Knutson, 2005). Other research focuses on consumers' decision-making processes and their corresponding brain activities (e.g., Yoon et al., 2006; Knutson et al., 2007; Hedgcock and Rao, 2009). Moreover, management researchers in the area of information system research have begun to use these neurophysiological methods and prior findings to investigate information system constructs, as well as users' decision making in the online world (e.g., Dimoka, 2011; Kopton et al., 2013; Riedl et al., 2014). Recently, management research focusing on organizational behavior has also begun to develop a new interdisciplinary perspective by transferring prior neurological findings to extend organizational theories (e.g., Boyatzis et al., 2014).

This body of research has generally resulted in significant developments by adding a new theoretical perspective to economic research. However, the majority of these well-known neuroeconomic studies, all of which investigate decision making from different economic perspectives (Glimcher et al., 2013), were commonly investigated through fMRI-based research (Braeutigam, 2012).

Nevertheless, fMRI measurements have limitations with regard to how their real-world applicability corresponds to restricted external validity, so that many researchers question whether economic decision making can truly be measured and generalized in such a restricted situation (Shimokawa et al., 2009; Ariely and Berns, 2010; Ayaz et al., 2013). Only limited conditions can be tested in the fMRI-scanner, and only specific types of stimuli can be shown while the participant is lying in the scanner. For this reason, neuroeconomic studies, to date, have primarily focused on “fictive” tasks and not “real-world” situations (Ariely and Berns, 2010). Because of the technical limitations, the influencing stimuli in these studies were often reduced in complexity, suggesting that other measurements are necessary for future field experiments in neuroeconomics.

Against this background, mobile functional near-infrared spectroscopy (fNIRS) measurement seems to have strong potential for applicability in field studies. fNIRS can be defined as a non-invasive optical brain imaging technique that investigates cerebral blood flow (CBF) as well as the hemodynamic response in a local brain area during neural activity (Jackson and Kennedy, 2013). In different prior studies it has been demonstrated that, comparable to functional magnetic resonance imaging (fMRI), the fNIRS method is a reliable and valid measurement for cortical activations (see Ernst et al., 2013).

In response to the limited research, this paper aims to integrate various disciplinary fNIRS studies in economic decision making research which, up to now, have been examined mainly in isolation from each other. Moreover, we provide insight into the potential of fNIRS for measuring brain activations during different real-world situations of economic decision making.

At present, researchers in (neuro-)economics are challenged by many new economic trends. Particularly, these trends increase the difficulty of using traditional neurophysiological methods such as fMRI to investigate research questions, for which measuring situational factors in the “real world” and outside the laboratory are highly important.

Three current examples of trends regarding consumers' economic decision making effectively illuminate new challenges of (neuro-)economic research:

These three examples of new trends in economic research show the relevance for new methods in (neuro-)economic research outside the laboratory. Overall, neuroeconomic research is challenged by a number of changes that demand new methodological development toward flexible and mobile technologies such as fNIRS.

An elaboration of the potential of fNIRS methods should begin by detailing the fNIRS methods that are applied for recording data and for analysis. This background both allows useful assessment of the potential of this new method, and brings to light its challenges. Jöbsis (1977) was first to explain how the optical measurement for cerebral hemodynamic response known as NIRS is performed by the irradiation of near-infrared light into participants' head and its scattering position (Villringer et al., 1993; Funane et al., 2013).

Near-infrared light, with a wavelength spectrum of circa 650–950 nm, passes through biological tissue without difficulty, and can non-invasively illuminate several centimeters of the tissue (Lloyd-Fox et al., 2010; Jackson and Kennedy, 2013; Scholkmann et al., 2013). Because of this characteristic transparency, the spectrum of near-infrared light is often called an “optical window” (Jöbsis-vanderVliet, 1999). In general, it can be approximated that oxy-(O2Hb) and deoxy-hemobglobin (HHb) are the main absorbers, so that the changes in oxy- and deoxy-hemoglobin can be assessed, allowing for the indirect quantification of neural activity (Jackson and Kennedy, 2013). Various existing studies calculate the optimal wavelength as well as the optimal number of different wavelengths for perfect illumination based on a mathematical optimization problem (Yamashita et al., 2001; Sato et al., 2004; Correia et al., 2010; Schelkanova and Toronov, 2012; Scholkmann et al., 2013). Based on these physical and mathematical calculations, several techniques have been developed to measure the hemodynamic response. The majority of these studies implement the continuous-wave method (Lloyd-Fox et al., 2010). For oxy-(O2 Hb) and deoxy-hemobglobin (HHb) chromophores, dissimilar near-infrared light absorption properties can be anticipated, so that by using the absorption variation difference resulting from different chemical structure changes in blood oxygenation of the illuminated skin, the skull and some centimeters of brain tissue can be measured (Jöbsis, 1977; Lloyd-Fox et al., 2010).

The near-infrared light sources, which are laser-emitting diodes, are placed directly onto a participant's scalp and are sent—in a “banana-shaped” form (Okada and Delpy, 2003)—to the detectors, called optodes. The depth and exactness of measurement depends on the distance between the source and the detector. In different mathematical models, the correlation of the inter-optode distance and the depth of light penetration are assumed to be proportional (Nossal et al., 1988; Ehlis et al., 2005). However, the larger the distance, the more the light is scattered, so that the detector should not be placed more than 3 cm away from the source (Lloyd-Fox et al., 2010; Jackson and Kennedy, 2013).

For the conversion of the raw near-infrared light absorption and attenuation data into oxy- and deoxy-hemoglobin concentration, the most commonly used algorithm is the modified Beer–Lambert law (Kocsis et al., 2006; Scholkmann et al., 2013). In contrast to the original Beer–Lambert law, which generally allows the quantification of concentration only for non-scattering media (Scholkmann et al., 2013), the modified Beer–Lambert law considers a constant optical scattering of the light and relates the change in chromophore concentration to the change in light attenuation (see also Figure 1): ΔA=α∗Δc∗L∗DFC  (Lloyd-Fox et al., 2010)

with A, light attenuation; α, absorption coefficient; c, concentration of specific chromophore; L, source-detector separation; DFC, differential path length factor, which may vary according to specific wavelength, gender, age and difference in tissue type (Duncan et al., 1995, 1996).

To measure changes in oxy- and deoxy-hemoglobin, the brain needs to be illuminated with two different wavelengths, which in turn need to be integrated into two simultaneous equations in order to measure the blood oxygenation differences in the tissue (Lloyd-Fox et al., 2010).

In addition to the implementation of the modified Beer–Lambert law, fNIRS data analysis requires further preprocessing methods such as motion artifact correction, low- and high-pass filtering (for eliminating breathing, heartbeat and drift; see also Piper et al., 2014) and single-channel signal-to-noise analyses. Similar to fMRI studies, fNIRS data needs to be Bonferroni-corrected for multiple comparisons between channels. This implies that the p-values of multiple comparisons are adapted by the number of correlations performed (see also Ernst et al., 2013).

A number of NIRS studies can be found in the literature, some using stationary NIRS machines and others using mobile, wireless and innovative NIRS prototypes. A general NIRS study classification separating “stationary” and “mobile” NIRS has been developed, designating specific subcategories of studies with emphasis on “economic decision making” and on “general decision making” (see Figure 2).

Based on these (Figure 2) classifications, the following section presents various NIRS studies.

As shown, very few studies have investigated economic decision making with fNIRS measures, nor have used portable mobile fNIRS measures that allow participants to freely move around in a naturalistic setting. Many researchers are still working on basic methodological studies to optimize the methodology and the analyses of the near-infrared light data. However, the characteristics of the measurements suggest that fNIRS has many strengths, and offers significant potential for neuroeconomics, particularly for research with a high external validity. In order to determine the suitability of using fNIRS, we provide a decision-table for judging the potential for integrating fNIRS studies into neuroeconomic research (see Table 3). Based on this decision-table, neuroeconomic researchers can assess the potential of fNIRS studies for answering their specific research questions.

In the following, we elaborate on the various assessment criteria of this decision-table:

In summary, the present discussion, in combination with the elaborated continuum (Table 3), offers neuroeconomic scientists a decision agenda for evaluating the potential of fNIRS methodology, and its usefulness for their specific research questions, aims, and areas of interests.

As discussed, fNIRS might be a promising and new tool for neuroeconomic research under certain circumstances, especially regarding mobile technologies. In the following, therefore, we will report first insight about a potential experimental set-up using mobile fNIRS in combination with further neurophysiological methods for studies outside the laboratory. The aim of this section is to provide neuroeconomists with greater ability for developing fNIRS studies.

For the application of a mobile fNIRS device in a field experiment, a wearable multi-channel fNIRS system with a specifically developed prefrontal cap is typically used. For our economic decision-making studies (with regard to the measurement restrictions of fNIRS in deeper brain regions), we are mainly interested in the prefrontal areas of the brain.

Even if scientists keep the design of a field experiment very simple and integrate no complex treatment conditions, questions arise regarding how to control external influencing variables, and how to reconstruct aspects such as consumer behavior for the analysis. These questions are extremely relevant, because consumers in field experiments can move freely and without exact timing conditions, which presents challenges for neurophysiological measurements. For the optimal measurement of consumers' decision making outside the laboratory, a number of reasons can be identified for developing a multifaceted experimental set-up not only with fNIRS measurement, but also with eye-tracking and EDA devices (Figure 3).

The eye-tracking device enables researchers to both follow consumers' eye movements objectively, and to develop a baseline for analysis of the field experiments. Thus, in a field experiment, for example, about consumers' decision making about innovative prototype products such as cars, the eye-tracking data can give important information regarding the stimulus that is being observed by the consumer at a specific time (in seconds) during the decision-making phase. With parallel eye-tracking measurements as baseline and timeline, therefore, participants' individual differences during the experiment outside the laboratory can be controlled. This element of integrating eye-tracking has potential usefulness in neurophysiological field experiments using a portable fNIRS device. Furthermore, comparing fNIRS activation and electrodermal arousal reactions (as additional controls) could be useful for measuring not only eye-tracking but also (“EDA”; e.g., Greenwald et al., 1989) simultaneously with fNIRS (Holper et al., 2014). Prior studies have revealed that specific activations in prefrontal areas have an effect on EDA (Tranel, 2000; Critchley, 2002; Figner and Murphy, 2010; Holper et al., 2014).

Finally, for the successful implementation of experiments outside the laboratory, specific additional operating procedures need to be considered. Consumers should close their eyes (“resting conditions”) before observing objects/stimuli (e.g., car exteriors/interiors), and should walk at a constant speed (to control for potential movement artifacts), in experiments with several rounds and a number of treatment conditions. Moreover, the supervisor of the experiment needs to simultaneously observe each participant, take notes regarding potential outliers (of external variables), and trigger the mobile fNIRS device concerning specific upcoming situations and pre-defined conditions. These operating procedures are relevant for subsequent successful data preprocessing.

Generally, most prior neuroeconomic studies were implemented with the fMRI scanner, but the fMRI technology also has limitations that have often been criticized—especially concerning the generalizability and the restrictions regarding the integration of situational factors in a lab, and regarding external validity (Braeutigam, 2012). Some of these limiting factors and critical aspects could be countered with new technological tools such as mobile fNIRS devices that support investigation of economic decision making outside the laboratory. Currently, however, few neuroeconomic studies applying mobile NIRS methods are available. The reason for this research gap might be that the use of mobile and wireless NIRS is in its early stages, and many researchers still work on prototypes for optimal data acquisition (e.g., Muehlemann et al., 2008; Atsumori et al., 2009; Piper et al., 2014). The aim of this paper was to demonstrate, and to discuss, the potential that fNIRS methods offer for neuroeconomic research questions in which situational factors outside the laboratory play a major role (e.g., in consumer decision making at the point of sale).

To fulfill our objectives, we reviewed existing studies with relevance to (economic) decision making and presented a decision-table for neuroeconomic researchers that may enable better determination of the suitability of fNIRS for studying neuroeconomics. To the best of our knowledge, this is the first article investigating the fNIRS method as a new and prospective tool for economic research questions outside the laboratory. By integrating studies from various disciplines, we developed a decision-table to support future application of fNIRS methods. Finally, we presented a first concept of a potential field experiment set-up for a neuroeconomic research question.

Overall, this present article shows that further research using (mobile) fNIRS for studies on economic decision making outside the laboratory could be a fruitful avenue. As well, the paper helps to validate the potential of a new method regarding different aspects and to develop a more effective application outside the laboratory.