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This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Purpose: This chapter introduces a choice modeling framework that explicitly represents the planning and action stages of the choice process.

Methodology: A discussion of evidence from behavioral research is followed by the development of a discrete choice modeling framework with explicit planning and action submodels. The plan/action choice model is formulated for both static and dynamic contexts; where the latter is based on the Hidden Markov Model. Plans are often unobservable and are treated as latent variables in model estimation using observed actions.

Implications: By modeling the interactions between the planning and action stages, we are able to incorporate richer specifications in choice models with better predictive and policy analysis capabilities. The applications of this research in areas such as driving behavior, route choice, and mode choice demonstrate the advantages of the plan/action model in comparison to a “black box” choice model in terms of improved microsimulations of behaviors that better represent real-life situations. As such, the outcomes of this chapter are relevant to researchers and policy analysts.

This paper aims to propose an approach for the optimization of imperfect preventive maintenance and corrective actions performed on a single machine. After maintenance, the machine returns to an age between “as good as new” and “as bad as old”.

The approach is based on fuzzy logic and simulation‐based optimization. Fuzzy logic is preferred over crisp logic because it is relatively easy to implement in this situation considering that the human factor is hardly interpreted by analytical methods because of its unpredictable nature. Simulation‐based optimization is used to have a more reactive and accurate tool for practitioners.

Taking into account the impact of the imperfections due to human factors, the period for preventive maintenance, which minimizes the expected cost rate per unit of time or maximizes the availability of the system, is evaluated by the simulation‐based optimization.

Different and more realistic maintenance levels must be considered and the traceability of a specific system could be used to determine the most appropriate failure law. For this study, cost reduction was considered as the priority, but the model can be adjusted according to the user's preferences.

This paper considers a single repairable machine as a system that undergoes periodic preventive and corrective maintenance actions. Considering maintenance imperfections, rule‐based fuzzy logic can be integrated into the maintenance program to determine the times for the periodic preventive maintenance actions.

Considering human factors in maintenance programs is indispensable to assure more accurate and realistic results. However, due to the difficulty engendered by their modeling, most theoretical maintenance models do not consider these factors. Therefore, the proposed fuzzy model in the paper can be an important tool to include them.

The purpose of this paper is to propose an integrated model of action learning based on an examination of four reviewed action learning models, definitions, and espoused outcomes.

A clear articulation of the strengths and limitations of each model was essential to developing an integrated model, which could be applied to Lynham's general method of theory‐building research in applied disciplines. The paper examined common themes according to the model structure, methods, and methodologies. The four models selected for this review were Gregory's Group Action Learning Process Model, Paton's Systemic Action Learning Cycle, Paton's Systemic Action Learning Spiral, and Watkins and Marsick's Continuous Learning Model.

A comparison of the key variations in the definitions of action learning and desired outcomes explained differences in model designs. HRD practitioners need a better understanding of the variables that affect the outcomes of action learning through exploring learning transfer issues and through testing multiple methodologies. Similarly, the integrated model was designed to indicate how change takes place within an organization, dictated by either internal or external factors. A description of the construction of the integrated model is provided.

Owing to the disconnect between the conceptual development and application phases of theory‐building research, more empirical evidence is needed to support the connection between action learning models and methodologies and desired outcomes. The integrated model was designed from a systems perspective with particular emphasis on soft systems in the problem and analysis phases to illustrate the role of organizational modeling of the relationships among members, processes, and the internal and external environment. HRD practitioners could re‐examine their decision making, particularly in approaching large‐scale change. HRD practitioners could document their specific approaches to action learning, including a combination of action research methods and soft systems methodologies. A comparison of outcomes versus the methodologies could be made.

The objective of the integrated action learning model is to improve decision making related to facilitating change from an HRD perspective, given the theories and principles underlying each model. The integrated model could serve as the basis for gaining new knowledge about critical systems theory and action research as it relates to action learning and change facilitation. It is the paper's intent that the proposed integrated model will spur further theory‐building research in employing action learning as an organizational change intervention.

Studies of entrepreneurial action often distinguish between different phases such as opportunity identification, evaluation, and exploitation. Yet, the richness of past contributions masks the absence of an integral framework to organize, in a theoretically consistent ensemble, the different kinds of cognitive processes that underpin entrepreneurial action. In this chapter, we draw from research on human action and cognition to offer an integrative model of the cognitive processes that foster entrepreneurial action. By presenting a more specific articulation of when, how, and why different cognitive processes operate, we provide theorists and empiricists with a more complete picture of how entrepreneurs’ thinking evolves from the emergence of an opportunity idea to the initiation of concrete entrepreneurial acts. In addition, our framework draws attention to cognitive inflection points that entrepreneurs must navigate in their journey toward entrepreneurship. By explicitly locating these inflection points and specifying the changes in mental processing that occurs at each point, we highlight that for entrepreneurial action to ensue, entrepreneurs must shift from one type of cognitive processing to another. Along this line, our model draws attention to the entire set of cognitive “skills” entrepreneurs must master for successful completion of each phase and successful transitions between phases.

This chapter examines employee learning behavior as a function of work characteristics. Karasek’s Demand-Control (DC) model proposes that high job demands and high job control are conducive to employee learning behavior. A review of 18 studies revealed that whereas most of these supported these predictions, methodological and conceptual shortcomings necessitate further study. Perhaps the most important weakness of the DC-based research on learning is that the conceptual foundations of the DC model regarding employee learning behavior are quite rudimentary, while the role of interpersonal differences in the learning process is largely neglected. The second part of this chapter explores the relationship between work characteristics and learning behavior from the perspective of German Action Theory (AT). AT explicitly discusses how work characteristics affect learning behavior and assigns a role to interpersonal differences. We conclude by presenting a model that integrates action-theoretical insights on learning with DC-based empirical results.

Robots face fundamental challenges in achieving reliable and stable operations for complex home service scenarios. This is one of the crucial topics of robotics methods to imitate human beings’ advanced cognitive characteristics and apply them to solve complex tasks. The purpose of this study is to enable robots to have the ability to understand the scene and task process in complex scenes and to provide a reference method for robot task programming in complex scenes.

This paper constructs a task modeling method for robots in complex environments based on the characteristics of the perception-motor memory model of human cognition. In the aspect of episodic memory construction, the task execution process is included in the category of qualitative spatio-temporal calculus. The topology interaction of objects in a task scenario is used to define scene attributes. The task process can be regarded as changing scene attributes on a time scale. The qualitative spatio-temporal activity graphs are used to analyze the change process of the object state with time during the robot task execution. The tasks are divided according to the different values of scene attributes at different times during task execution. Based on this, in procedural memory, an object-centered motion model is developed by analyzing the changes in the relationship between objects in the scene episode by analyzing the scene changes before and after the robot performs the actions. Finally, the task execution process of the robot is constructed by alternately reconstructing episodic memory and procedural memory.

To verify the applicability of the proposed model, a scenario where the robot combines the object (one of the most common tasks in-home service) is set up. The proposed method can obtain the landscape of robot tasks in a complex environment.

The robot can achieve high-level task programming through the alternating interpretation of scenarios and actions. The proposed model differs from traditional methods based on geometric or physical feature information. However, it focuses on the spatial relationship of objects, which is more similar to the cognitive mechanism of human understanding of the environment.

Models can be helpful for understanding the essence and/or processes of complex phenomena in a field, because they are simple representations of concept patterns and relationships. This paper presents a generic model for designing action learning programs, including action research projects, within organizations. Core values for action learning programs and explanations for success and potential pitfalls are also discussed. The model has been tested and found useful by many senior managers, academics, postgraduates and consultants in several countries. Readers are encouraged to explore the model in order to apply, refine, adapt it, or to create their own model representing their theoretical framework, concepts, values and systems thinking.

This study examined the effect that venture creation action has on the outcomes of nascent entrepreneurship. A conceptual model was developed which proposes action as a fundamental mechanism in venture creation. Thus, action should rightly be considered as a means which transmits the effects of venture resource endowments on to venture creation outcomes. This conceptual model was empirically supported in a random sample of nascent ventures. Ventures with higher levels of human or social capital were found to be more active in venture creation. In turn, more active venture attempts were more likely to achieve improved venture creation outcomes. Further, human and social capital, on their own, exhibit little direct influence on the venture outcomes achieved. These findings confirm action's central place in the venture creation process.