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The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment.

A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers.

The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice.

The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial.

Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation.

This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.

The aim of this study is to identify factors that influence drivers' route choice response to travel time information about both the expressway and local streets provided by variable message signs on arterial roads.

A stated preference questionnaire survey was conducted to collect behavioral data. The generalized estimating equations (GEEs) method with a logit link function was used to model driver response and account for correlations within repeated observations from the same respondent. Four GEEs‐based estimations with different working correlation structures were conducted and compared with each other as well as the conventional maximum likelihood estimation.

Driving experiences, expressway delays, causes of delay, and the number of traffic lights on local streets are factors influencing route choice decisions. A new finding is that there exist differences in response behavior among employer‐provided car, taxi and private car drivers. On the modeling aspect, the exchangeable structure was the most appropriate in this study.

This study indicates the effectiveness and appropriateness of the GEEs method and suggests further examination of GEEs' performance.

The route choice probability model established by this study will facilitate better investment, design and assessment of dynamic information services in transportation management.

The dynamic information this study concerns has rarely been addressed in the literature. Little literature to date has applied the GEEs method in information response modeling. This study reaches solider conclusions about the GEEs method.

The purpose of this research is to test how varying the numbers of attributes and alternatives affects the use of heuristics and selective information processing in discrete choice experiments (DCEs). The effects of visual attribute and alternative non-attendance (NA) on respondent choices are analyzed.

Two laboratory experiments that combined eye tracking and DCEs were conducted with 109 and 117 participants in the USA. The DCEs varied in task complexity by the number of product attributes and alternatives.

Results suggest that participants ignore both single attributes and entire alternatives. Increasing the number of alternatives significantly increased attribute NA. Including NA in choice modeling influenced results more in more complex DCEs.

The current experiments did not test for choice overload. Future studies could investigate more complex designs. The choice environment affects decision-making. Future research could compare laboratory and field experiments.

Private and public sectors often use DCEs to determine consumer preference. Results suggest that DCEs with two alternatives are superior to DCEs with four alternatives because NA was lower in the two-alternative design.

This empirical research examined effects of attribute and alternative NA on choice modeling using eye tracking and DCEs with varying degrees of task complexity. Results suggest that accounting for NA reduces the risk of over- or understating the impact of attributes on choice, in that one avoids claiming significance for attributes that might not truly be preferred, and vice versa.

Much of the research conducted in logistics/SCM has focused on satisfaction/retention of customers. This has left a critical gap for managers: before customers can be satisfied and ultimately retained, a purchase choice of logistics services has to occur. To date, very little research has addressed how logistics customers make purchase choice decisions about logistics services. The purpose of this paper, using logistics research methods, is to introduce adaptive choice modelling (ACM) to address this gap and put forth a research method that is useful for academic researchers and logistics/SCM managers.

This paper provides an overview of ACM, along with a discussion of its important research advantages, limitations, and practical applications. Additionally, an empirical demonstration of this research technique is provided to illustrate how academic researchers and logistics managers can use ACM to better understand the decision‐making process of customers when selecting logistics services.

In order to demonstrate this research technique, a research project was designed and implemented that analyzed the choice process of consumers selecting parcel carriers to ship a textbook. The results show that price, speed of delivery, and tracking are the three most important variables in the selection decision. The results also show that consumers are not homogeneous, but can be divided into five distinct need‐based segments. Recognizing and understanding the nature of these segments should help managers better meet the needs of parcel shippers.

The main research limitation with this study is that it is based on a convenience sample; thus future research will need to replicate this study to confirm the research findings. However, the ultimate purpose of the study is to present a new research method and discuss how to apply this method, so that logistics/SCM practitioners and academic researchers can better understand customers of logistics/SCM services. Thus, while the nature of the sample is a limitation, it should be viewed in this context.

While conjoint analysis has existed for decades, this technique has rarely been implemented by logistics/SCM researchers and practitioners. Instead, logistics/SCM researchers and practitioners have focused more on retention methods and have virtually ignored modelling the actual purchase choice of logistics/SCM services. New advancements in conjoint analysis, specifically the ACM approach, have many important and unique advantages and applications for logistics/SCM researchers and practitioners. ACM has not been used in a logistics/SCM context.

The authors develop a logit modeling approach, designed for application to UPC scanner panel data, to assess the effects of coupon promotions on consumer brand choice. The effects of coupon promotions are captured via two measures: the prevailing level of availability and the prevailing face value of coupons for each brand. Both of these measures are derived from coupon redemptions of a separate sample of households. The approach captures both the advertising effect and the price discount incentive of a coupon. It also avoids drawbacks of previous choice models which have incorporated coupon effects by subtracting the value of a redeemed coupon from the price of the brand purchased. The authors illustrate their modeling approach on data for two product categories: catsup (light coupon usage) and liquid laundry detergent (heavy coupon usage). Findings are reported for coupon users and non‐users as well as across latent segments.

Online privacy research has seen a focus on user behavior over the last decade, partly to understand and explain user decision-making and seeming inconsistencies regarding users' stated preferences. This article investigates the level of modeling that contemporary approaches rely on to explain said inconsistencies and whether drawn conclusions are justified by the applied modeling methodology. Additionally, it provides resources for researchers interested in using computational modeling.

The article uses data from a pre-existing literature review on the privacy paradox (N = 179 articles) to identify three characteristics of prior research: (1) the frequency of references to computational-level theories of human decision-making and perception in the literature, (2) the frequency of interpretations of human decision-making based on computational-level theories, and (3) the frequency of actual computational-level modeling implementations.

After excluding unrelated articles, 44.1 percent of investigated articles reference at least one theory that has been traditionally interpreted on a computational level. 33.1 percent of all relevant articles make statements regarding computational properties of human cognition in online privacy scenarios. Meanwhile, 5.1 percent of all relevant articles apply formalized computational-level modeling to substantiate their claims.

The findings highlight the importance of formal, computational-level modeling in online privacy research, which has so far drawn computational-level conclusions without utilizing appropriate modeling techniques. Furthermore, this article provides an overview of said modeling techniques and their benefits to researchers, as well as references for model theories and resources for practical implementation.

Dolphin excursions have become increasingly popular worldwide. Many past studies assessing the value of dolphin excursions use choice-based methods such as the conjoint analysis. However, this method is often criticized as being hypothetical. The purpose of this paper is to describe a relatively low cost but effective approach to enhance understanding of consumer preference obtained by conjoint analysis. The method relies heavily on using internet-based survey tools.

Enabled by an online tool, individuals are asked to self-explicate their preferred alternatives using the same attributes as are found in the conjoint design. The difference between the self-constructed, preferred alternatives and those offered in conjoint experiment are incorporated into choice models. Unlike previous research where only rough estimates can be provided, the proposed method allows precise capture of respondents’ preferred alternative through the automated online survey design.

Results show that although the extra effort involved in data collection is small, the gain in model fit, choice interpretation, and the value (welfare) estimation is sizeable. Evidence indicates that consumers would be willing to pay up to $50 more for adventurous excursions and guarantees that they will interact with dolphins could worth up to $70 per trip. The approach presented in this paper can also serve as a method to test for preference consistency.

This study is the first using an online survey to assess values associated with dolphin excursion. It describes the benefit of involving online tools to enhance modeling and interpretation of consumer behavior. Applications of internet-based surveys on household consumer products are abundant (such as food and electronics) but this study offers a much less discussed application in environmental service.

The assumption of free will in contemporary economics is an important starting point for socio‐economic analysis in contrast to methodologies which assume that human action is pre‐determined by forces beyond individual control. However, contemporary economic theory is devoid of choice in critical domains with important implication for economic analyses and public policy, given the ancillary assumption of the importance of market forces in determining choice behavior. The purpose of this paper is to argue that freedom of choice exists given traditional constraints such as relative prices and income.

This is a theoretical paper examining the assumption of free will in choice behaviour in economic theory. It makes reference to literature in economics and philosophy that shed light on this critical working assumption in economics.

Conventional analysis pays little heed to non economic constraints on human action that affect and delimit but do not preclude free choice or free will. Of vital importance to free will in choice behavior are institutions which delimit the extent of coercion in the decision‐making process.

An important implication for research is the determination of the necessary and sufficient conditions for the existence of free will in choice behaviour. Given the existence of free will and free choice, individuals are morally responsible for their choices. It is therefore important to determine the extent which free will exists and that which constrains free will in choice behaviour.

This paper challenges the extremes of the free will debate in economics and suggests the boundaries within which free will exists in economic behaviour. It also suggests the welfare implications of limitations on free will where no negative externalities exist.

Traditionally, economic production models consider pollution as bads that may be modeled as either outputs or inputs in economic models. The purpose of this paper is to examine the implications of these modeling choices on the measurements of productive efficiency and private costs of pollution control.

The authors apply the hyperbolic distance functions to measure trucking efficiency and the private costs of pollution control.

The results show: (i) regardless of the choice of modeling, when only one bad was incorporated in hyperbolic distance functions, the efficiency loss and private abatement cost measures derived from the two models were equivalent, but potential pollution reduction and good output expansion differed; (ii) when more than one bad were introduced, the equivalence of efficiency loss measure in (i) did not hold; and (iii) the potential amounts of pollution reduction and good output expansion were larger when bads were modeled as inputs. With multiple bads, private abatement costs varied considerably under the two modeling treatments.

From a policy standpoint, the results suggest that one should consider the modeling options with caution when multiple economic bads are involved, because the resulting measures of economic burden of pollution control differ.

The paper shows that the traditional conceptual framework for modeling pollution in hyperbolic distance functions could yield inconsistent results.