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This paper investigates how respondents to conjoint experiments process information and choose among product profiles, and how this varies with their knowledge about the product. Models for estimating conjoint attribute weights are almost exclusively based on principles of compensatory decision making. The paper aims to explore to what extent and in what way these basic principles of conjoint modelling are violated.

Data were obtained from a verbal protocol study where 18 undergraduate students each performed a total of 28 stated choice tasks while “thinking aloud”.

Results show that cognitive operations consistent with compensatory decision rules constitute a majority of the total number of operations performed across tasks and respondents. However, few respondents exhibited a consistent use of compensatory‐type processes throughout their choice sets. Results suggest that individual preferences interact with characteristics of the choice sets to instigate changes in information processing. It also appears that complete strategies are seldom used. Finally, respondents' knowledge about the product influences the cognitive operations that respondents use in solving conjoint tasks.

Results are based on responses from 18 undergraduate students, which makes generalizations hard.

One implication of this work is that one should apply a more flexible model framework to allow detecting the existence of non‐compensatory strategies.

This paper is one of few which aim to implement findings in behavvioral decision research within the context of conjoint analysis.

Increasing evidence suggests that choice behaviour in real world may be guided by principles of bounded rationality as opposed to typically assumed fully rational behaviour, based on the principle of utility-maximization. Under such circumstances, conventional rational choice models cannot capture the decision processes. The purpose of the chapter is to propose a modeling framework that can capture both decision outcome and decision process.

The modeling framework incorporates a discrete cognitive representation structure and implies several decision heuristics, such as conjunctive, disjunctive and lexicographic rules. This allows modeling unobserved decision heterogeneity involved in a single decision, for example, in the form of a latent-class specification, taking into account mental effort, risk perception and expected outcome as explanatory factors.

Two models based on this framework are applied to decision problems underlying pedestrian shopping behaviour and compared with conventional multinomial logit models. The results show that the proposed models may not be superior to logit models in terms of model selection criteria due to the extra complexity in selecting heuristics, but suggest more interesting insights to the underlying decision mechanisms.

Understanding decision processes additional to outcomes is a promising research direction. A more developed model should take into account more contextual and socio-demographic factors in the heuristic selection part. The assumptions of information processing must be subject to empirical tests to validate the model.

The proposed modeling framework bridges the long-existing contradicting approaches in the field of decision modeling, namely the rational approach and the bounded rational approach, by proving that non-compensatory decision heuristics can be inferred from compensatory model formulations with discretized information representations and decision criteria assumed. It also incorporates a heuristic choice part into the decision processes in the form of latent-class specifications and shows the viability of the new modeling framework.

Operational practices and operational capabilities are critical yet distinct elements in operations strategy. The purpose of this paper is to examine their conceptual differences and explore how they are developed in a portfolio, considering the potential for practices and capabilities to be either compensatory or additive in nature.

The compensatory model argues that the lack of investments in certain practices or capabilities can be offset by higher level of investments in other practices or capabilities. In contrast, the additive model argues that the firm must invest in certain practices or capabilities and that trade‐offs are impossible. The authors examine evidence for these two competing models using an approach borrowed from studies of multi‐attribute consumer preference models and statistical comparisons of non‐nested models.

Data for the study were collected from operations managers who were members of a large professional organization. The findings indicate that the effects of operational practices are additive for some operational outcomes and compensatory for others. However, the combinatorial nature of operational capabilities is purely compensatory.

The results imply that adequate investment in a wide range of operational practices is necessary to enhance operations performance. However, operations units appear to have more flexibility in choosing to develop a distinctive operational capability set.

The study clarifies the different orientation of operational practices and operational capabilities as they contribute to operations strategy. The findings provide guidelines regarding the combinatorial natures of operational practices and operational capabilities. These guidelines have critical strategic implications for resource allocation schemes and how these schemes affect operational performance.

Despite the widespread use of mobile government (m-government) services in developed countries, the adoption and acceptance of m-government services among citizens in developing countries is relatively low. The purpose of this study is to explore the most critical determinants of acceptance and use of m-government services in a developing country context.

The unified theory of acceptance and use of technology (UTAUT) extended with perceived mobility and mobile communication services (MCS) was used as the theoretical framework. Data was collected from 216 m-government users across Bangladesh and analyzed in two stages. First, structural equation modeling (SEM) was used to identify significant determinants affecting users' acceptance of m-government services. In the second stage, a neural network model was used to validate SEM results and determine the relative importance of the determinants of acceptance of m-government services.

The results show that facilitating conditions and performance expectancy are the two important precedents of behavioral intention to use m-government services, and performance expectancy mediates the relationship between MCS, mobility and the intention to use m-government services.

Academically, this study extended and validated the underlying concept of UTAUT to capture the adoption behavior of individuals in a different cultural context. In particular, MCS might be the most critical antecedent towards mobile application studies. From a practical perspective, this study may provide valuable guidelines to government policymakers and system developers towards the development and effective implementation of m-government systems.

This study has contributed to the existing, but limited, literature on m-government service adoption in the context of a developing country. The predictive modeling approach is an innovative approach in the field of technology adoption.

Organizations deal with digital technologies to achieve their strategic goals. The shift toward digitization is a major challenge because it requires companies to create a digital outlook that influences organizational design. As a result, investigation of institutional theory and entrepreneurial orientation theory in the European food industry has become the focus of research in recent times.

To this end, data were collected from 83 companies related to the food industry in the European context. By applying a hybrid phase of the partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods, this study captured the causal–non-linear relationships among the study constructs.

The findings revealed that the variables of institutional theory and entrepreneurial orientation theory affect the adoption of the digital strategy. There is also a dual interaction role for e-business capabilities and digital transformation. The results of non-linear relationships confirmed that digital strategy adoption is highly influenced by digital transformation, followed by risk-taking, digital leadership, e-business capabilities, organizational agility, proactiveness and innovativeness.

The authors provided significant implications for practitioners and academics about the most influential determinants of digital strategy – businesses must move swiftly toward digitization across its various units to achieve their objectives. An organization’s leadership must realize that equipping the employees with necessary skills is the first step toward digitalization.

The current study underscores the digital strategy, which is usually an overlooked area of investigation, in the food industry. The study identifies some important predictors of digital strategy adoption with the interaction’s role of digital transformation and e-business capabilities. Such relationships have been rarely discussed. In addition, the adoption of a hybrid SEM-AAN approach makes the study an original one.

Quality management (QM) practices have long been suggested as a package to improve the quality performance of a company. Yet, empirical studies offered little support of this perspective as only a small set of QM practices were found to be conductive to quality improvement. By taking a new approach, the purpose of this paper is simultaneously evaluate the individual and synergistic effects of several key quality practices on operations performance.

Survey data were collected and used to test the proposed hypotheses. Hierarchical regression analyses were performed to assess the magnitude of synergistic effects and individual effects.

It is found in this study that the synergistic effects across QM practices are substantial in enhancing all operations performance dimensions whereas individual effect varies for different performance goals.

This study makes theoretical contributes to the field of QM by providing empirical support of the interdependence assumption of QM practices and resolving the inconsistent findings. Practically, the results suggest that in-depth implementation of various QM practices simultaneously can be highly effective to achieve performance improvement on all accounts. In addition, the positive additional individual effects also open up a firm’s strategic choices of where to focus the resource investment in the adoption process as long as the strategic focus can justify its contribution toward the desired performance goals.

This paper addresses the issue of mass‐customisation from the point of view of consumer demand. It aims to develop a framework to examine the demand side of the mass‐customisation equation which will allow researchers to identify whether a market of customers who are ready for mass‐customised products exists. In doing so it considers in particular three “inconveniences” of mass‐customisation: the increased price of customised products; the delay in receipt of custom‐made products; and the need for customers to invest time in specifying their preferences before the product can be produced.

To evaluate the comparative effectiveness of perceptions‐based market segmentation strategies: to what extent do consumers' choice rules and the distinctness and variability of consumer preferences determine the success or failure of PBMS strategies?

The computer simulation is run on an artificial consumer market. Its firm and consumer agents enjoy a certain extent of autonomy and a limited capability of learning. Strategies for incorporating the choice information into the firms' segmentation schemes, consumers' brand choice rules, initial preference patterns and their variability over time are factors in the experimental design.

The market factors “brand choice rule” and “distinctness” and “adaptivity” of preferences significantly influence the profit performance of the segmentation and positioning strategies. The distinctness of the initial pattern of consumer preferences turns out to be least influential while the choice rule is most important.

Computer simulation cannot replace analyses of real‐world data. When, however, advanced explanatory models are made to fit to empirical data the results sometimes are disappointing (and then do not get published). Computer simulation on artificial markets assists in exploring the reasons for success or failure.

Boundedly rational consumers; product classes which are technologically homogeneous and subject to communications‐driven differentiation; consumer preferences that are directly inaccessible and must be inferred from actual brand choice; consumers' perceptions and preferences evolving over time are realistic settings.

Controlling for conditions such as the consumers' choice rules and the distribution and variability of preferences in real markets demands a prohibitive research effort. No empirical study so far has tried to systematically relate the profit performance of marketing strategies to choice rules and preference distinctness and variability.