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The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.

Service brands are increasingly dominating the economy. However, there is limited knowledge about how to grow service brands, and whether growing service brands will require strategies different from the strategies that are based on goods brand research. To address this knowledge gap, this paper conceptualizes a “ServBrand triad” based on the service literature, and empirically demonstrates how its three drivers are related to service brand growth. Service brand growth is defined as positive changes in brand outcomes, such as the brand being liked more, used more, or more profitable or valuable to a firm. The empirical work utilizes 11 years of longitudinal brand and firm data that are analyzed by dynamic multivariate generalized method of moments (GMM) panel models. The findings confirm the importance of the three drivers of service brand growth: quality, personalization, and relationships. Service marketers are advised to emphasize relationship-based service personalization (as opposed to quality-based personalization), maintain consistent service quality “at” customer expectation, neither above nor below, throughout the relationship, and improve service quality gradually (or subtly) to avoid quality-cost tradeoff and quality inconsistency perceptions. These findings contribute to an improved understanding of the factors that drive service brand growth, and how those factors differ from the factors that drive goods brand growth.

The capability of AI is currently expanding beyond mechanical and repetitive to analytical and thinking. A “Feeling Economy” is emerging, in which AI performs many of the analytical and thinking tasks, and human workers gravitate more toward interpersonal and empathetic tasks. Although these people-focused tasks have always been important to jobs, they are now becoming more important to an unprecedented degree. To manage more effectively in the Feeling Economy, managers must adapt the nature of jobs to compensate for the fact that many of the analytical and thinking tasks are increasingly being performed by AI, and, thus, human workers must place increased emphasis on the empathetic and emotional dimensions of their work.

Most developed countries are now in the last years of the Thinking Economy. As physical labor became automated, human physical labor became devalued. This created a Thinking Economy, in which the most valued human skill was thinking. We still mostly live in the Thinking Economy, but there are starting to be cracks. AI is beginning to make impressive inroads in thinking, which is now threatening the economic value of human thinking. Thinking AI can be usefully categorized into analytical thinking and intuitive thinking. Modern machine learning is making rapid advances in analytical thinking, but has much more trouble with intuitive thinking, which may require a different approach. This means the last human bastion of dominance in the Thinking Economy is intuitive thinking and common sense.

Jobs are changing, as AI assumes more thinking tasks. With thinking no longer an area of human dominance, human workers are being driven to increase the feeling and interpersonal content of what they do. From the viewpoint of AI intelligence, those jobs are “upgraded” to focus on the tasks that are more difficult for AI. Empirical research shows that the importance of feeling tasks is increasing for human workers, and that feeling-related jobs are increasing the fastest in terms of pay.

The current educational system is designed to produce Thinking Economy workers. That has not always been true, and it will soon not be true again. The Physical Economy emphasized very different skills. When the Physical Economy was superseded by the Thinking Economy, the educational system was forced to adapt. A similar adaptation is required today, as the Feeling Economy begins to overtake the Thinking Economy. Skills that are highly prized today may soon be worth much less, which suggests the need for a revolution in education. In the Feeling Economy, the most prized skills are likely to be empathy, emotional intelligence, communication, and interpersonal relationships. This will require a different kind of education. Many of today’s Thinking Economy workers will be faced with a massive dislocation, as the skills they need to compete change to Feeling Economy skills. This suggests a heightened need for retraining and continuing education.

Many people think of AI as a tool that businesses employ, and of course business actually is applying AI very widely. However, consumers also use AI, and it is having a profound impact on how regular people live their lives. What’s more, AI is changing people’s very nature in a systematic way. Consumers in an AI-dominant world are very different from the consumers of previous generations. We consider such things as the impact of the smartphone (“AI in a pocket”) and how it allows consumers to focus more on feeling and interpersonal relationships.

If feeling AI gets good enough, it may become even better than humans on that dimension. At that point, AI would dominate HI with respect to all three intelligences—mechanical, thinking, and feeling. That point is known in the literature as the “singularity,” and it would take us beyond the Feeling Economy. The implications of the singularity would be profound. There are a number of implications that follow logically from our multi-intelligence view of AI. For example, the singularity may result in inequality of wealth and income that makes today’s situation seem benign. Even more concerning, we may find that humans do not have the control over AI that they think they do. Ultimately, AI may see itself as existing to serve itself, rather than the human race. Authors, philosophers, and other intellectuals are divided about whether the singularity will ultimately be good or bad for humans. One scenario has AI doing all the work, while humans enjoy lives of leisure. Another favorable scenario has AI augmenting and transforming humans, with the result that our powers are expanded. On the other hand, there are doomsday scenarios in which the human race is effectively replaced by AI, which may then be thought of as the next stage of human evolution and development.

As the Feeling Economy emerges, and AI assumes more thinking tasks, people are increasingly focusing on emotion. One notable way this trend is manifested is the increasing use of emoticons (typographical tricks that resemble pictures) and emoji (plug-in graphics that are actual pictures). Such emotional communication has become ubiquitous on social media and the Internet. Those technologies have deconstructed time, physical distance, and emotional distance, making emotional connection possible even when people are far apart, and even when their communication is separated in time. This greater access to emotional communication makes emotional intelligence more important than ever. We even see the emotionalization of the creative arts, such as music, as AI becomes more involved in artistic production.

As we move from the Thinking Economy to the Feeling Economy, management will have to change. The key to this new era is that AI will no longer be seen as subservient to humans. Instead, humans and AI will be teammates that collaborate to get work done. AI will be seen as an equal member of the team, and will increasingly assume more of the team’s thinking tasks. This teamwork will be more collaboration than augmentation, with AI increasingly seen as an equal member of the team.

As the Feeling Economy emerges from the development of AI, one group of people seems uniquely positioned to benefit. We may be beginning to experience the era of women. The history of automation and AI turns out to involve a steady decline in the status and importance of men, along with a steady increase in the status and importance of women. The Physical Economy was dominated by men, with their larger size and bigger muscles. As physical tasks were automated, the Thinking Economy emerged, which was a much more favorable environment for women. Throughout the Thinking Economy, signs of women’s progress have been accelerating, reflected by such things as women’s right to vote, a proposed Equal Rights Amendment and Title IX in the United States, and increasing participation by women in the military, sports, business, academia, and politics. This progress is sure to accelerate further, as women assume unprecedented status and importance in the Feeling Economy.

As the Feeling Economy emerges, there are likely to be quite a few new issues that will arise, that will challenge us morally and ethically, and will create new demands with respect to governance. Although the Feeling Economy will create new opportunities, it will also cause serious job dislocations. Related to this is the threat of increasing income and wealth inequality. The rise of Thinking AI may also result in unintended consequences that arise from our imperfect understanding of how to manage AI. One such consequence is the possibility of bias and discrimination resulting even from totally rational algorithms. Also under threat will be our privacy. New technologies are almost always used for war, also, which is likely to form the basis for a new arms race. There is also the possibility that AI can go rogue, in ways unanticipated. There is also the concern that AI may cause harm, leading to the issue of how to manage the liability arising from damages arising from the use of AI. Finally, there is the possibility of “robot rights,” which is likely to become an issue as AI becomes more intelligent.

As AI assumes an increasing number of thinking tasks, eventually including even intuitive thinking tasks, humans will find that their highest and best use involves emotion, empathy, and interpersonal relationships. At that point, we will be in a Feeling Economy, in which AI specializes in thinking, and humans specialize in feeling. We are not entirely there yet, but the trend is already clear, and supported by empirical data. We project, based on the current rate of change, that feeling tasks should be more important than thinking tasks for human workers by the year 2036.

AI intelligence does not stop at thinking. Many tech companies are competing to develop feeling AI. Feeling AI is AI that has the capabilities to (1) recognize, (2) simulate, and (3) react appropriately to emotions. The current feeling AI is more mature for recognizing emotions than for simulating and responding to emotions appropriately. This is because recognizing emotions involves simple extensions of machine learning, such as data mining and text mining, with the difference being that it is emotional data that are analyzed. Examples include affective state detection (used for detecting driver’s driving conditions), classification or prediction of affective state, visualization for affective data, and biometric and behavioral sensors (e.g., eye tracking, heart rate, keystroke, and mouse tracking). Responding appropriately to emotions, such as human–computer interaction (HCI) and conversational AI, requires more human-like capabilities that are currently challenging for machines.

A typical firm is operated by multiple functional managers who may collaborate as well as compete to achieve firm performance. In the digital age, firm performance is essentially customer-dependent and technology-dependent, with both marketing and information technology (IT) playing key roles. Unfortunately the two functions often have very different worldviews. We show how these differences can damage firm performance, and suggest ways to mitigate this damage. We build a worldview difference model, synthesized from multiple disciplines. The model is tested using both matched and nonmatched observations from marketing and IT managers, and is analyzed with hierarchical linear models using both perceptual and objective firm performance data over a 4-year period. We find that differences between the beliefs and perceptions of marketing managers and IT managers generate a negative impact on firm performance, and suggest appropriate technology-culture associations to effectively align their worldviews for firm performance. To improve firm performance, a cross-functional appreciation for market and technology drivers can be achieved by making marketing managers more learning-oriented and by providing IT managers a culture that is congruent with technology.

Flow, a construct borrowed from reference disciplines, has been proposed as the central process in Web navigation. However, when flow is applied directly to the context of marketing, it suffers from conceptual ambiguity and overlap with the popular marketing construct, involvement. The present study aims to provide a clear framework within which to delineate the relationship between flow and involvement, and thus develop a better theoretical basis that incorporates the flow construct into Internet marketing. This study first distinguishes explicitly between flow, enduring involvement, and situational involvement. It then operationalizes these constructs with the use of customary measures to disclose their unique and common characteristics. Finally, a second-order confirmatory factor analysis allowing measurement errors is used to identify explicitly the tripartite relationship between the three constructs. The conceptual and operational implications are discussed. © 2006 Wiley Periodicals, Inc.

Why do some consumers stick with a certain brand(s), whereas others are prone to switch? This study proposes, and empirically examines, the hypothesis that the structure of the evoked set is the nonconscious memory representation of consumers' repeat purchase behavior. Two causes of repeat purchase are identified: Brand loyalty, which is inherent to consumers and is constant over time, and inertia, which fluctuates with marketing variables. A model selection procedure is performed to compare the relative efficiency of using multinomial and binary logit models to examine the theoretical accuracy of the hypotheses. Findings suggest a U-shaped relation: Consumers who hold moderately intercorrelated evoked sets are intrinsically brand loyal, but are susceptible to the impact of marketing variables. They are intrinsically prone to switch, but less open to marketing variables, when their evoked sets are high or low in intercorrelation. The findings contribute to the understanding of both conscious and nonconscious aspects of repeat purchase patterns. © 1999 John Wiley & Sons, Inc.

This study proposes, and empirically examines a new typology of emotional global appeals in terms of consumers' similarity of response. Each basic emotion possesses an evolutionary survival value, is associated with a different pattern of autonomic nervous system activity and facial expression, and is acquired early in the socialisation process. Basic emotions are universal in all demographic groups. Alternatively, social emotions are the blends of basic emotions. They are culturally specific because they are the social reactions that are acquired later in the socialisation process. Six Benetton ads were used to generate three pairs in the basic-social emotional hierarchy: happiness versus humour, love versus warmth, and sadness versus surprise. Two hundred and forty-seven male and female undergraduate consumers from the US, UK, Taiwan, and mainland China served as the subjects. Ads that employ basic emotions (happiness, love, and sadness) are found to generate more similar emotional responses among globa...

We consider the implications of the three pillars of sustainability (environment, economy and social justice) on consumption in a wealthy country. Building a theoretical model that includes consumers, business, government, the environment, and economic and political relations between nations, we explore how sustainability should affect the consumption behavior of consumers, charitable aid to poorer countries, and responsible environmental practices by businesses. Our model enables us to provide normative implications for consumers, society and business. Importantly, we assume that all stakeholders will optimize their self-interest, and that altruism will only partly explain behavior consistent with sustainability. Among the more non-obvious findings are that (1) the poorer the poor countries are, the less the rich countries should consume, (2) the more sensitive the global political climate is to economic inequity between the rich and poor nations, the less the rich countries should consume, and (3) if aid to poor countries is effective enough, then the more materialistic the society is, the more charitable aid it should give. We also confirm a number of more intuitive findings, such as that business should use more green technology as the taxes on pollution and/or efficiency of green technology increase, and the more resource-intensive consumption is, the less consumers should consume. Taken as a whole, the findings imply that societal consumption patterns should be sensitive to aspects of environmental impact and social justice, even if altruistic motivations are absent.

In this article, I seek to provide a perspective concerning the study of electronic commerce (EC). EC can be viewed as primarily concerned with the phenomena of electronic transactions. Four properties of EC are elaborated on surrounding this core: 1. for EC actors to transact in IT enabled and constrained networked socio-technical markets; 2. for information-based assets that are priced on an equivalent value mechanism; 3. for maximisation of both aggregate welfare and voluntary transactions; 4. for modelling with a welfare analysis approach to balance allocation efficiency and equality.