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As part of consumer behavior, the buying decision process is the decision-making process used by consumers regarding the market transactions before, during, and after the purchase of a good or service. It can be seen as a particular form of a cost–benefit analysis in the presence of multiple alternatives.[1][2]

To put it simply, In consumer behavior, the buyer decision process refers to the series of steps consumers follow when making choices about purchasing goods or services, including activities before, during, and after the transaction.

Common examples include shopping and deciding what to eat. Decision-making is a psychological construct. This means that although a decision cannot be "seen", we can infer from observable behavior that a decision has been made. Therefore, we conclude that a psychological "decision-making" event has occurred. It is a construction that imputes a commitment to action. That is, based on observable actions, we assume that people have made a commitment to effect the action.

Nobel laureate Herbert A. Simon sees economic decision-making as a vain attempt to be rational. Simon claimed (in 1947 and 1957) that if a complete analysis is to be done, a decision will be immensely complex. Simon also wrote that peoples' information processing ability is limited. The assumption of a perfectly rational economic actor is unrealistic. Consumers are influenced by emotional and nonrational considerations making attempts to be rational only partially successful. He called for replacing the perfect rationality assumptions of homo economicus with a conception of rationality tailored to cognitively limited agents.[3] Even if the buyer decision process was highly rational, the required product information and/or knowledge[4] is often substantially limited in quality or extent,[5][6] as is the availability of potential alternatives. Factors such as cognitive effort and decision-making time also play a role.[6][7][8][9]

Stages

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Consumers shopping at London's Burlington Arcade engage in a variety of recreational and functional purchasing activities – from window shopping through to transporting their purchases homewards.

The five stages of a decision process were first introduced by philosopher John Dewey in How We Think in 1910.[10] Later studies expanded upon Dewey's initial work and are seen as foundational for analysis of consumer purchasing decision-making.[11] Dewey did not refer in How We Think specifically to purchasing decisions, but in applied terms his five stages are:

  • Problem/Need Recognition – recognize what the problem or need is and identify the product or type of product which is required.[12] For example, A university student realizes their laptop has become too slow to run design software, prompting the need for a new, more powerful model.
  • Information Search – the consumer researches the product which would satisfy the recognized need.[12] Example: The student starts browsing tech review websites, comparing brands, and checking specifications and prices online.
  • Evaluation of Alternatives – the consumer evaluates the searched alternatives. Generally, the information search reveals multiple products for the consumer to evaluate and understand which product would be appropriate.[12] Example: They narrow choices down to three laptops, weighing trade-offs between price, features, and customer reviews.
  • Purchase Decision – after the consumer has evaluated all the options and would be having the intention to buy any product, there could be now only two things which might just change the decision of the consumer of buying the product that is what the other peers of the consumer think of the product and any unforeseen circumstances. Unforeseen circumstances for example, in this case, could be financial losses which led to not buying of the product.[12] Example: The student decides to buy a mid-range model from a brand with strong support and warranty policies.
  • Post Purchase Behavior – after the purchase, the consumer may experience post-purchase dissonance feeling that buying another product would have been better. Addressing post-purchase dissonance spreads the good word for the product and increases the chance of frequent repurchase.[12] Example: After using the new laptop for a few weeks, the student shares their experience in an online review, expressing satisfaction or regret depending on performance.

These five stages are a framework to evaluate customers' buying decision process. While many consumers pass through these stages in a fixed, linear sequence, some stages such as evaluation of alternatives may occur throughout the purchase decision.[13] The time and effort devoted to each stage depend on a number of factors including the perceived risk and the consumer's motivations. In the case of an impulse purchase, such as the purchase of a chocolate bar as a personal treat, the consumer may spend minimal time engaged in information search and evaluation and proceed directly to the actual purchase.[14]

The rise of digital media and social networks are changing the way that consumers search for product information.

Problem/need-recognition

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Problem/Need-recognition is the first step in the buying decision. Without knowing what the customer needs, they will not be enticed to purchase the product. The need can be triggered by internal stimuli (e.g. hunger, thirst) or external stimuli (e.g. advertising).[14]  Maslow held that needs are arranged in a hierarchy. According to Maslow's hierarchy, only when a person has fulfilled the needs at a certain stage, can he or she move to the next stage. The problem must be the products or services available. It's how the problem must be recognized.

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The information search stage is the next step that the customers may take after they have recognized the problem or need in order to find out what they feel is the best solution. The field of information has come a long way in the last forty years,[when?] and has enabled easier and faster information discovery.[citation needed] Consumers can rely on print, visual, and/or voice media for getting information.

Evaluation of alternatives

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Shoppers inspect the quality of fresh produce at a market in Jerusalem.

At this stage, consumers evaluate different products/brands on the basis of varying product attributes, and whether these can deliver the benefits that the customers are seeking.[14]  This stage is heavily influenced by one's attitude, as "attitude puts one in a frame of mind: liking or disliking an object, moving towards or away from it".[14]  For example, in high-involvement purchases such as buying a car, consumers may compare numerous models, read reviews, and seek expert opinions; whereas for low-involvement purchases like toothpaste, they may rely on brand familiarity or promotional cues.

Customer involvement High Medium Low
Characteristics High Medium Low
Number of brands examined Many Several Few
Number of sellers considered Many Several Few
Number of product attributes evaluated Many Moderate One
Number of external information sources used Many Few None
Time spent searching Considerable Little Minimal

Purchase decision

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This is the fourth stage, where the purchase takes place. According to Kotler, Keller, Koshy, and Jha (2009),[14] the final purchase decision can be disrupted by two factors: negative feedback from other customers and the level of motivation to comply or accept the feedback. For example, after going through the above three stages, a customer chooses to buy a Nikon D80 DSLR camera. However, because his good friend, who is also a photographer, gives him negative feedback, he will then be bound to change his preference. Secondly, the decision may be disrupted due to unanticipated situations such as a sudden job loss or the closing of a retail store.

Post-purchase behavior

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These stages are important to keeping customers. Customers match products with their experiences on whether they are either content or discontent with the product. This affects the decision process for resemblant purchases from the same company in the future,[15] mainly at the information search stage and evaluation of alternatives stage. If brand loyalty is made then customers will often fast-tracked or skip completely the information search and evaluation of alternative stages.

Either being content or discontent, a customer will spread good or bad opinions about the product. At this stage, companies try to make favorable post-purchase communication to encourage the customers to purchase.[16] 

Also, cognitive dissonance (consumer confusion in marketing terms) is common at this stage; customers often go through the feelings of post-purchase psychological tension or anxiety. Questions include: "Have I made the right decision?", "Is it a good choice?", etc.

Models of buyer decision-making

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Making a few last minute decisions before purchasing a gold necklace from a Navy Exchange vendor

There are generally three ways of analyzing consumer buying decisions:

  • Economic models – largely quantitative and are based on the assumptions of rationality and near perfect knowledge. The consumer is seen to maximize its utility. See consumer theory. Game theory can also be used in some circumstances.
  • Psychological models – psychological and cognitive processes such as motivation and need recognition. They are qualitative rather than quantitative and build on sociological factors like cultural influences and family influences.
  • Consumer behavior models – practical models used by marketers. They typically blend both economic and psychological models.

In an early study of the buyer decision process literature, Frank Nicosia (Nicosia, F. 1966; pp. 9–21) identified three types of buyer decision-making models. They are the univariate model (He called it the "simple scheme".) in which only one behavioral determinant was allowed in a stimulus-response type of relationship; the multi-variate model (He called it a "reduced form scheme".) in which numerous independent variables were assumed to determine buyer behavior; and finally the "system of equations" model (He called it a "structural scheme" or "process scheme".) in which numerous functional relations (either univariate or multivariate) interact in a complex system of equations. He concluded that only this third type of model is capable of expressing the complexity of buyer decision processes. In chapter 7, Nicosia builds a comprehensive model involving five modules. The encoding module includes determinants like "attributes of the brand", "environmental factors", "consumer's attributes", "attributes of the organization", and "attributes of the message". Other modules in the system include consumer decoding, search and evaluation, decision, and consumption.

In recent years, the rise of digital ecosystems has led to the development of the Online Consumer Decision Journey (OCDJ) model. This model highlights how digital touchpoints—such as social media, influencer content, and recommendation algorithms—disrupt the traditional linear decision-making path. For instance, McKinsey’s Circular Decision Journey (2009) emphasizes that post-purchase experience feeds directly into future decision-making, forming a continuous loop rather than a straight line.

Some neuromarketing research papers examined how to approach motivation as indexed by electroencephalographic (EEG) asymmetry over the prefrontal cortex predicts purchase decision when brand and price are varied. In a within-subjects design, the participants have presented purchase decision trials with 14 different grocery products (seven private labels and seven national brand products) whose prices were increased and decreased while their EEG activity was recorded. The results showed that relatively greater left frontal activation (i.e., higher approach motivation) during the decision period predicted an affirmative purchase decision. The relationship of frontal EEG asymmetry with purchase decision was stronger for national brand products compared with private label products and when the price of a product was below a normal price (i.e., implicit reference price) compared with when it was above a normal price. The higher perceived need for a product and higher perceived product quality were associated with greater relative left frontal activation.[17]

For any high-involvement product category, the decision-making time is normally long and buyers generally evaluate the information available very cautiously. They also utilize an active information search process. The risk associated with such a decision is very high.[18]

Neuroscience

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Neuroscience is a useful tool and a source of theory development and testing in buyer decision-making research. Neuroimaging devices are used in Neuromarketing to investigate consumer behavior.[19]

See also

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References

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Bibliography

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[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Buyer decision process

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The buyer decision process, also known as the consumer decision-making process, encompasses the sequential steps that individuals or groups follow when identifying a need, gathering information, evaluating options, making a purchase, and reflecting on the outcome for goods or services. This framework is central to understanding consumer behavior in marketing, as it highlights how psychological, social, and environmental factors shape purchasing choices. Typically outlined in five stages, the process begins with problem recognition, where a discrepancy arises between the consumer's current state and desired state, triggering awareness of a need—such as prompting a purchase or a malfunctioning device signaling replacement. The second stage, information search, involves seeking data from internal sources like personal memory or external ones such as reviews, advertisements, or recommendations to reduce uncertainty. In the evaluation of alternatives, consumers compare options based on attributes like , , and , often employing decision rules to narrow choices. The purchase decision follows, where the selected option is bought, potentially influenced by last-minute factors like stock availability or promotions. Finally, post-purchase behavior assesses satisfaction, leading to , , or , which can affect future decisions and perceptions. This model, popularized by scholars like , varies in complexity depending on the purchase type—ranging from routine low-involvement buys (e.g., groceries) to extensive high-involvement ones (e.g., cars)—and is influenced by cultural, personal, and situational elements. Influential theoretical frameworks, such as the Howard-Sheth model () emphasizing perceptual and learning constructs or the Engel-Blackwell-Miniard model () integrating broader influences, have refined it over decades to predict and guide strategies. In digital eras, online tools and increasingly accelerate these stages, underscoring the process's adaptability to evolving consumer environments.

Introduction

The buyer decision process is a multi-stage framework describing how consumers recognize a problem or need, search for information, evaluate alternatives, decide on a purchase, and assess the outcome afterward. This concept was originally conceptualized in the by marketing scholars, with introducing behavioral models for buyer analysis in 1965 and Engel, Kollat, and Blackwell presenting a comprehensive consumer behavior model in their 1968 publication. Historically, the process evolved from early economic models in the 1940s grounded in rational choice theory, which portrayed consumers as utility-maximizing agents making logical decisions based on complete information and preferences. By the , the framework shifted to incorporate behavioral influences, such as psychological attitudes, learning, and environmental factors, as seen in expanded models like Howard and Sheth's theory of buyer behavior. Key milestones include Engel et al.'s 1968 introduction of a decision-making integrating inputs, , and outputs, which laid the groundwork for modern studies. In marketing, the buyer decision process holds critical importance by guiding strategies for product development, targeted advertising, and customer retention, while connecting to the wider discipline of consumer behavior research that informs predictive analytics and segmentation. Understanding this process enables marketers to align interventions with consumer psychology, enhancing satisfaction and loyalty across the five core stages of need recognition, information search, evaluation, purchase, and post-purchase reflection.

Core Stages of the Buyer Decision Process

Problem or Need Recognition

The problem or need recognition stage marks the inception of the buyer decision process, where consumers perceive a discrepancy between their current state and a desired state, prompting the of purchase considerations. This recognition arises from internal stimuli, such as physiological needs like or , or emotional drives like dissatisfaction with an existing product, and external stimuli, including advertisements, social influences, or environmental cues that highlight unmet needs. In seminal models of consumer behavior, this stage is described as a passive yet critical trigger, often involving a dynamic where internal imbalances or external prompts disrupt equilibrium and motivate action. This process integrates with Abraham Maslow's hierarchy of needs, framing consumer motivations hierarchically from basic physiological requirements—such as food and shelter, exemplified by demand for organic products from brands like —to higher-level aspirations like (e.g., Volvo's emphasis on features), social belonging, esteem through status symbols, and via personal growth products like Nike's inspirational . The level of involvement influences the intensity of recognition: in routine decisions, such as restocking groceries, needs emerge habitually with minimal discrepancy awareness due to low personal stakes; in limited decision scenarios, like purchasing a following life changes such as family expansion, recognition involves moderate from evolving circumstances. Marketing tactics have historically accelerated need recognition since the 1960s, with aspirational advertising targeting esteem and needs to evoke desires for luxury or status-enhancing goods, as seen in early campaigns promoting lifestyle elevation. promotions, such as limited-quantity or time-sensitive offers, further intensify external stimuli by creating urgency and perceived unavailability, prompting immediate awareness of potential loss. These strategies, rooted in psychological reactance, effectively shift passive consumers toward active decision-making. In the information search stage of the buyer decision process, consumers actively or passively gather to address the recognized need, drawing on both internal and external resources to inform potential solutions. Internal search involves recalling from , such as past experiences with similar products or services, which serves as the initial and often for low-involvement decisions where familiarity reduces the need for further effort. External search, in contrast, entails seeking new beyond personal knowledge, triggered when internal resources prove insufficient and motivated by factors like or desire for validation. Consumers typically consult four main categories of external sources during this phase. Personal sources include advice from , friends, or acquaintances, which are highly influential due to perceived trustworthiness and in social contexts. Commercial sources encompass marketer-controlled channels like , salespeople, and retailer displays, aimed at persuading consumers toward specific options. Public sources involve impartial outlets such as online forums, , and government publications, valued for objective insights. Experiential sources allow direct interaction, such as handling or testing products in stores, providing tangible evaluation opportunities. The extent of information search varies significantly based on several determinants, including perceived risk, involvement level, and time availability. Higher perceived risk—such as financial or performance uncertainties in durable goods—prompts more extensive search to mitigate potential losses, as evidenced in studies across product categories like . Product involvement, reflecting the personal relevance or emotional attachment to the purchase, similarly drives greater effort; high-involvement decisions, like buying a , lead to broader external consultations compared to routine low-involvement buys. Time availability acts as a constraint, with ample time enabling deeper exploration, while pressures limit search to efficient sources. Historically, information search in the pre-digital era, particularly the , relied heavily on print media like newspapers and magazines, alongside physical visits to stores, due to limited communication options and high search costs from travel or inquiries. The advent of the transformed this into hybrid searches combining digital tools with traditional methods, enabling rapid access to vast data via search engines and social platforms. In the , modern consumers typically draw from an average of 5.8 sources per discovery process, reflecting the integration of online reviews, ads, and word-of-mouth in multifaceted searches. This evolution has intensified search for high-stakes purchases while streamlining routine ones, ultimately feeding into the evaluation of alternatives.

Evaluation of Alternatives

In the evaluation of alternatives stage, consumers systematically compare potential options gathered during the information search to determine which best satisfies their needs. This process typically involves two primary modes: attribute-based evaluation, where consumers conduct a detailed, feature-by-feature of alternatives (e.g., assessing laptops by processor speed, battery life, and price), and attitude-based evaluation, which relies on holistic feelings or overall impressions toward the options without granular attribute analysis. Attribute-based processing is more common for high-involvement purchases requiring deliberate trade-offs, while attitude-based processing suits low-effort decisions driven by gut reactions. Consumers apply either compensatory or non-compensatory models to simplify this comparison. Compensatory models allow trade-offs, where strengths in one attribute (e.g., superior ) can offset weaknesses in another (e.g., higher ), often leading to a weighted overall score for each alternative. In contrast, non-compensatory models reject such balancing; for instance, the elimination-by-aspects removes options failing a minimum threshold on a key attribute (e.g., discarding all cars without ), regardless of excelling elsewhere. These models help manage , with non-compensatory approaches dominating when time or information is limited. Key evaluation criteria include price, perceived quality, brand reputation, and overall value, which consumers weigh against their priorities to narrow options. This assessment occurs within the consumer's evoked set—the subset of alternatives deemed acceptable and actively considered—distinct from the inert set (known but indifferent options) and inept set (dismissed as unsuitable). The evoked set, typically small (3-7 items), forms the basis for comparison, influencing how criteria like or value-for-money shape preferences. For low-involvement purchases, consumers often rely on heuristics to expedite evaluation, bypassing exhaustive analysis. The leads to favoring options easily recalled from memory (e.g., recently advertised brands), while the judges alternatives based on stereotypical fit (e.g., assuming a premium offers superior quality due to its image). These mental shortcuts, though efficient, can introduce biases, such as overemphasizing vivid but unrepresentative examples. Consumer attitudes toward alternatives are quantitatively assessed using multi-attribute models, such as Fishbein's (1967) expectancy-value approach, which computes an overall attitude score as the sum of strengths multiplied by attribute evaluations across relevant dimensions. The formula is: A=∑i=1n(bi×ei)A = \sum_{i=1}^{n} (b_i \times e_i) where AA is the overall attitude, bib_i is the consumer's about the extent to which the alternative possesses attribute ii, and eie_i is the evaluation of that attribute's desirability (typically on a scale from -3 to +3). This model predicts preference by aggregating weighted , providing a structured metric for compensatory evaluation.

Purchase Decision

The purchase decision stage represents the culmination of the decision-making process, where an intention to buy forms and the commits to acquiring a specific product or service from the evoked set—the limited subset of alternatives actively considered after evaluation. This selection is heavily influenced by the , which posits that behavioral intention, and thus the purchase decision, is determined by three core factors: the individual's attitude toward the behavior (evaluative beliefs about the outcome), subjective norms (perceived social pressures from others), and perceived behavioral control (the sense of ease or difficulty in performing the action). According to this model, a favorable attitude combined with supportive social norms and high perceived control strengthens the intention to purchase, leading the to choose from the evoked set based on these psychological drivers. For instance, a evaluating smartphones might select a particular if they hold positive attitudes about its features, perceive approval from peers, and feel confident in affording it. However, this transition from intention to actual purchase can be disrupted by intervening factors, including latent or unattended attitudes that emerge subconsciously during the final moments, as well as shifts in subjective norms or perceived control. External obstacles, such as product stockouts, can derail the decision by forcing consumers to either delay the purchase, switch alternatives, or abandon it altogether, with studies showing that out-of-stock situations often lead to lost sales in retail environments. Similarly, unexpected changes at the point of transaction can alter perceived value and control, prompting consumers to reconsider or , as indicates that even small fluctuations influence buying intentions by heightening sensitivity to costs. Purchase decisions vary by type, ranging from impulse buys—characterized by low involvement, emotional triggers, and spontaneous action without prior —to deliberate decisions, which involve high involvement, rational analysis, and careful weighing of options. Impulse purchases, often driven by immediate , account for a significant portion of retail , such as unplanned grabs at checkout counters, while deliberate ones dominate high-stakes scenarios like buying after extensive . In online contexts, impulse decisions may occur via quick-add-to-cart features, whereas deliberate ones can falter due to checkout friction, exemplified by e-commerce cart abandonment rates averaging around 70% in the early , primarily from complex processes or unexpected fees. Timing and location further shape the purchase decision through environmental cues, such as point-of-purchase displays in physical stores, which capture attention and spur impulse buys by highlighting promotions or novel products at the moment of truth. In , similar influences arise from streamlined interfaces or urgency prompts, though excessive steps can exacerbate abandonment, underscoring the need for frictionless experiences to convert intentions.

Post-Purchase Behavior

Post-purchase behavior refers to the consumer's reactions, evaluations, and actions following the acquisition of a product or service, which can significantly influence future purchasing patterns and relationships. This phase involves assessing whether the purchase met expectations, managing any ensuing psychological discomfort, and deciding on product usage, , or disposal. Consumers may experience satisfaction or dissatisfaction, leading to behaviors ranging from to complaints, while the eventual disposal of products ties back into broader consumption cycles by potentially triggering new needs. A key psychological element in post-purchase behavior is , a state of tension arising from inconsistencies between pre-purchase beliefs and post-purchase realities, such as when a justifies an expensive purchase despite doubts about its value. Leon Festinger's theory posits that this discomfort motivates individuals to reduce dissonance through rationalization—such as emphasizing positive product attributes—or, in severe cases, leading to returns or . For instance, after buying a high-end , a user might downplay minor flaws to align their actions with their as a savvy decision-maker. This theory underscores how unresolved dissonance can erode trust in the brand, prompting avoidance in future purchases. Consumer satisfaction in the post-purchase phase is often measured using the expectation-disconfirmation model, which calculates satisfaction as the difference between expected performance and actual performance: positive disconfirmation (performance exceeds expectations) yields satisfaction, while negative disconfirmation results in dissatisfaction. Developed by Richard L. Oliver in 1980, this model highlights how unmet expectations, even for minor features, can amplify dissatisfaction, whereas confirmed or surpassed expectations foster . Tools like surveys assessing perceived performance against benchmarks are commonly employed to quantify this in . The outcomes of post-purchase behavior bifurcate into positive and negative trajectories, each with implications for advocacy. Positive outcomes, such as high satisfaction, drive customer loyalty, evidenced by repeat purchases, positive word-of-mouth recommendations, and increased spending; meta-analyses show satisfaction correlates strongly with retention rates (r = 0.60). Conversely, dissatisfaction triggers negative actions like product returns, formal complaints, or disparaging reviews, which can reduce loyalty and harm reputation—unhappy customers tell about twice as many people about their bad experiences as happy customers do about good ones. These dynamics position post-purchase experiences as pivotal for cultivating advocates who amplify efforts through organic endorsements. At the product's lifecycle end, post-purchase behavior extends to disposal decisions, where consumers weigh options like , resale, , or disposal based on environmental attitudes, perceived value, and convenience. indicates that less than half of used garments are collected for reuse or , and only 1% of used clothes are recycled into new clothes as of 2025. These choices not only reflect ethical considerations but also loop back to influence future need recognition, as disposing of an item may highlight gaps in possessions and restart the decision process. Emotional processing during disposal, including feelings of loss or guilt, can further shape these behaviors, linking to insights from studies.

Influencing Factors

Psychological Factors

Psychological factors refer to the internal cognitive and emotional processes that shape how individuals interpret stimuli and make purchase decisions. These factors operate subconsciously and consciously, influencing the entire buyer decision process by filtering information, driving needs, forming preferences, and guiding responses to products and brands. Key elements include , , learning, attitudes, personality, and , each drawing from established psychological theories applied to consumer contexts. Perception involves the selection, organization, and interpretation of sensory information, which consumers do selectively due to limited cognitive capacity. Selective attention directs focus toward stimuli relevant to current needs or interests, such as noticing advertisements for products that address unmet desires while ignoring others. Selective distortion occurs when consumers twist incoming information to align with preexisting beliefs or expectations, for instance, interpreting ambiguous product claims favorably if they match prior positive experiences. Selective retention ensures that only information supporting current attitudes is remembered, reinforcing biases over time. The 1950s debate on subliminal advertising, sparked by James Vicary's unverified claims of hidden messages boosting sales, raised concerns about subconscious perception influencing behavior, though empirical studies have since demonstrated its effects are minimal and context-dependent. Product design influences, including uniqueness and visual appeal, significantly affect consumer perceptions and buying decisions by capturing selective attention and enhancing perceived value. For example, high-design-aesthetic products elicit stronger positive emotional responses and higher purchase intentions compared to those with low aesthetics, as evidenced by event-related potential studies showing differences in brain activity during evaluation. Motivation underlies the activation of goal-directed in consumers, stemming from innate and learned needs that propel . Freudian posits that unconscious drives, mediated by the id (primitive impulses), ego (reality principle), and superego (moral standards), subtly influence consumption choices, such as selecting luxury items to satisfy repressed desires for status or pleasure. framework, outlined in his 1943 seminal paper, applies to consumers by suggesting they prioritize fulfilling physiological needs (e.g., basic sustenance products) before advancing to safety, social, esteem, and needs, which manifest in preferences for security-oriented or aspirational brands. Herzberg's , originally from 1959, extends to by differentiating factors (e.g., reliable product quality that prevents dissatisfaction) from motivators (e.g., innovative features that enhance satisfaction and loyalty). Learning and attitudes shape how consumers acquire knowledge about products and develop enduring evaluations. Classical conditioning, pioneered by Pavlov, links neutral stimuli (e.g., a brand logo) to unconditioned responses (e.g., pleasure from a product), fostering automatic positive associations in advertising. Operant conditioning, developed by Skinner, reinforces behaviors through rewards or punishments, such as repeat purchases driven by loyalty program incentives. Attitudes, as learned predispositions, form through these processes and can change via persuasion; the Elaboration Likelihood Model (ELM), proposed by Petty and Cacioppo in 1986, describes two routes—central (deep processing of arguments when motivation is high) and peripheral (cues like celebrity endorsements when elaboration is low)—affecting long-term attitude shifts toward brands. Personality and lifestyle further personalize decision processes by reflecting enduring traits and patterns of living. Personality traits, such as openness or extraversion from the Big Five model, predict varying responses to marketing appeals, with extraverted individuals more receptive to social or experiential promotions. Innovativeness as a trait, highlighted in Rogers' 1962 theory, determines adoption speed, categorizing consumers into innovators (risk-takers who adopt early), early adopters, early majority, late majority, and laggards based on compatibility with personal traits and values. , encompassing activities, interests, and opinions, segments consumers psychographically; the VALS framework, developed by in 1978, classifies them into groups like achievers (goal-oriented, brand-loyal) or experiencers (novelty-seeking, impulsive), guiding tailored marketing strategies.

Social and Cultural Factors

Social and cultural factors play a pivotal role in shaping the buyer decision process by influencing perceptions, preferences, and behaviors through interpersonal relationships and societal norms. Reference groups, defined as individuals or groups that consumers identify with or aspire to emulate, exert significant influence on purchasing choices. These groups are categorized into primary groups, such as and close friends, which provide direct, ongoing interactions and normative pressures; secondary groups, like professional associations, that offer informational cues through less intimate connections; and aspirational groups, which consumers seek to join, motivating purchases that signal affiliation or status. Opinion leaders within these groups, often characterized by expertise or centrality in social networks, accelerate the of product by sharing evaluations and endorsements, thereby reducing perceived in decisions. Family structures represent a core reference group, particularly in household purchases, where decision-making involves multiple roles within the family unit. Key roles include the initiator, who identifies the need; the influencer, who provides input on options; the decider, who makes the final choice; the buyer, who executes the purchase; and the user, who consumes the product. These roles vary by product type and family dynamics, with joint decision-making common for high-involvement items like automobiles or appliances. Since the , evolving dynamics have shifted traditional patterns, with increased female participation in financial and major purchase decisions due to greater workforce involvement and changing societal norms, leading to more egalitarian processes in many households. Culture and subcultures further mold buyer decisions by embedding core values, symbols, and rituals that guide consumption patterns. encompasses shared beliefs and practices that define acceptable behaviors, such as rituals around gift-giving during holidays that drive seasonal purchases. Subcultures, including ethnic or regional groups, add layers of specificity, like dietary preferences influencing food choices. Cross-cultural variations are evident in Hofstede's cultural dimensions, which incorporate social values and traditions that significantly influence purchasing decisions by shaping consumer preferences and behaviors aligned with group harmony and community welfare. For instance, high in the United States fosters independent decision-making and self-expressive purchases, contrasting with collectivism in many Asian societies and other collectivist cultures like Brazil, which prioritizes group harmony, family-oriented selections, and community-oriented traditions. These dimensions explain differences in appeals and product adaptations across markets. Social class, stratified by income, education, and occupation, profoundly affects consumer preferences by dictating access to resources and desired social positioning. Upper-class consumers often favor for subtle status maintenance through quality and exclusivity, while middle-class buyers may opt for aspirational items that blend affordability with prestige. Lower-class segments prioritize functionality and value, though conspicuous consumption can emerge as a signaling mechanism to elevate perceived status. For instance, luxury brands serve as costly signals of , enhancing social interactions and self-presentation in high-status groups, as supported by signaling theory applications in consumption studies. These class-based preferences influence and strategies.

Personal and Situational Factors

Personal factors, including demographics such as age, occupation, economic situation, and , significantly shape the buyer decision process by influencing needs, preferences, and . Age affects consumption patterns, with younger consumers often prioritizing innovative and experiential products, while older individuals focus on health and reliability. Occupation determines functional requirements, such as professionals in high-mobility roles opting for durable or attire suited to their work environment. The family life cycle further modulates these decisions across stages like bachelorhood, newly married couples, full nest (with young or older children), and , where priorities shift from personal indulgences to family-oriented necessities like or . , encompassing values and activities, provides a psychographic lens; the VALS framework, developed in 1978 by , segments consumers into types like achievers or experiencers to predict behavior based on motivations rather than demographics alone. Situational variables introduce temporary contexts that alter decision-making, including physical surroundings like store lighting and layout, which can enhance perceived product appeal and encourage impulse buys. Social settings, such as with family or peers, foster or shared evaluations, often leading to compromises in selections. Time pressure accelerates choices toward familiar options, while mood—elevated by positive stimuli or dampened by stress—impacts tolerance and spending willingness. Economic influences, particularly income levels and financing availability, constrain or expand options in the decision ; higher incomes enable luxury pursuits, whereas limited resources heighten focus on value. sensitivity varies with these factors, where consumers with modest incomes exhibit greater responsiveness to discounts or installment plans, prioritizing affordability over premium features. Temporal aspects, including seasonal variations and event-driven triggers, create episodic surges in decision-making, such as heightened demand for winter apparel or holiday gifts due to cultural timing. These patterns reflect how external calendars synchronize with personal needs, prompting proactive planning or reactive purchases around events like back-to-school periods.

Models of Buyer Decision-Making

Traditional Models

The traditional models of buyer decision-making emerged in the mid-20th century, framing the process as structured, often linear sequences influenced by psychological, economic, and environmental factors. These frameworks, developed primarily in the and , emphasized rational and external stimuli, providing foundational tools for despite later criticisms. The Engel-Kollat-Blackwell (EKB) Model, introduced in 1968 and popularized as the purchasing decision theory by Kotler and Keller, conceptualizes buyer behavior as a five-stage decision process: problem recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. It incorporates inputs from external stimuli such as efforts and social influences, which feed into the central decision process, and outputs in the form of actual purchase and satisfaction outcomes. Feedback loops allow post-purchase experiences to influence future problem recognition and searches, creating a dynamic yet primarily sequential structure. This model was later refined in the Engel-Blackwell-Miniard (EBM) model, with editions up to 2000, which expands on individual decision processes by integrating central (attitudes, ) and peripheral (demographics) influences on information processing and choice. This framework serves as the basis for hypotheses in empirical studies positing significant partial and simultaneous influences of cultural factors (such as Hofstede's dimensions encompassing social values and traditions), product design (emphasizing uniqueness and visual appeal), and price (focusing on perceived value and affordability) on purchasing decisions. The Howard-Sheth Model, published in , expands on buyer behavior by integrating perceptual and learning elements into an input-output system. Inputs are divided into significants (tangible attributes like and ) and symbols (intangible cues such as and image), which enter a "" of internal processes. Perceptual constructs within the black box handle input processing through mechanisms like and comprehension, while learning constructs shape long-term attitudes, intentions, and brand preferences. Outputs include purchase decisions, brand choices, and store selections, emphasizing how repeated interactions lead to habitual buying. In contrast, the views buyer decisions through the lens of rational maximization, assuming consumers act as economic agents with to optimize satisfaction under constraints. For high-involvement decisions, such as durable purchases, buyers evaluate options to maximize a function, often simplified as U=f(q,p)U = f(q, p), where qq represents of and pp incorporates price influences on perceived value. This model prioritizes cost-benefit analysis over psychological nuances, treating decisions as calculable equilibria. These traditional models faced significant critiques in the for their assumption of linearity and rationality, which overlooked emotional drivers and situational contexts in real-world buying. Scholars like Bozinoff (1982) argued that consumers frequently skip stages or apply minimal effort to low-involvement purchases, undermining the sequential framework. Additionally, Burns and Gentry (1990) highlighted the models' neglect of perceptual and affective influences, while (1990) pointed to their failure to address incomplete information or adaptive strategies in dynamic environments.

Contemporary Models

Contemporary models of the buyer decision process, developed primarily after , shift from linear sequences to nonlinear, iterative frameworks that reflect the influence of digital technologies and ongoing consumer-brand interactions. These models recognize the circular nature of , where consumers revisit earlier stages based on new inputs from sources, social networks, and personalized experiences, extending beyond a single transaction to include and loops. By incorporating elements like search behaviors and post-purchase sharing, they provide marketers with tools to engage consumers at multiple touchpoints throughout extended journeys. The Customer Decision Journey (CDJ), introduced by in 2009, exemplifies this evolution as a nonlinear model comprising initial consideration, active evaluation, moment of purchase, post-purchase experience, and a feedback loop to . In the initial consideration stage, consumers form an initial set of brands based on awareness from or word-of-mouth; active evaluation involves dynamic research where options are added or eliminated using peer reviews and retailer information; the purchase moment integrates final influences like pricing and availability; post-purchase assesses satisfaction and drives loyalty; and reinforces future considerations through recommendations. Derived from surveys of nearly 20,000 consumers across industries such as automobiles and , the CDJ emphasizes how touchpoints vary by category and geography, urging marketers to prioritize consumer-driven content over traditional . Complementing the CDJ, Google's Zero Moment of Truth (ZMOT) framework, published in 2011, highlights the critical pre-purchase inspiration phase enabled by online research. ZMOT describes the moment when consumers turn to search engines, videos, and to explore products before any physical encounter, effectively inserting a "zero" phase before the traditional first moment of truth (in-store discovery). This model stresses the role of digital visibility in capturing consumer intent early, with examples like shoppers researching appliance reviews via searches influencing up to 80% of high-consideration purchases in some categories. Based on 's analysis of evolving and retailer data, ZMOT advocates for brands to create searchable, shareable content to win these exploratory interactions. Recent advances in contemporary models integrate (AI) for and factors to address complexities. AI-driven recommendation algorithms, such as those using to suggest products based on browsing history and preferences, enhance and purchase stages by reducing overload and increasing . For instance, in functional food purchases, AI directly boosts purchase intention while mediating through trust and perceived value. Similarly, models incorporate considerations, where environmental impact is rarely a primary factor in alternative for categories like apparel, with studies showing consideration rates as low as 5-17% despite growing , often overridden by factors like price. These elements extend the and ZMOT by embedding ethical and technological dimensions into circular journeys, fostering long-term bonding through aligned values and tailored experiences.

Neuroscience in Buyer Decisions

Neuroimaging Techniques

Neuroimaging techniques provide objective measures of activity during , enabling researchers to observe responses to stimuli without relying on self-reported data. These methods, rooted in , have been applied in to analyze how buyers process information, evaluate options, and form purchase intentions. Common tools include (fMRI), (EEG), and complementary biometrics like eye-tracking, which together offer insights into emotional and cognitive engagement. Functional magnetic resonance imaging (fMRI), introduced in the early 1990s, measures changes in blood oxygenation level-dependent (BOLD) signals to detect active brain regions by tracking blood flow variations. This technique achieves spatial resolutions of 1-10 mm, allowing identification of emotional centers such as the , which is involved in processing affective responses during product evaluation. In , fMRI has been used in purchasing tasks where participants view products and prices, revealing activations in areas like the that predict buying behavior with high accuracy. A seminal study by Knutson et al. (2007) demonstrated that ventral activity during product consideration enhances sales forecasting for consumer goods by up to 28.6% compared to traditional models. Electroencephalography (EEG) records electrical activity from the via electrodes, providing high (milliseconds) for real-time analysis of responses to stimuli like advertisements. Its portability—using devices such as the Emotiv EPOC+ with 14 channels—makes it suitable for naturalistic ad testing outside lab settings, capturing immediate reactions during purchase simulations. EEG analyzes frequency bands, including (8-13 Hz) for relaxation or disengagement and beta waves (13-30 Hz) for active , with frontal asymmetries indicating approach and levels in contexts. Systematic reviews highlight EEG's role in predicting ad and liking through these metrics, outperforming surveys in objectivity. Eye-tracking complements neuroimaging by monitoring gaze patterns, fixations, and pupil dilation as proxies for visual and emotional arousal, using cameras to track metrics like dwell time and saccades. Pupil dilation, for instance, increases with motivational intensity, signaling heightened in product features. Biometrics such as heart rate variability further quantify arousal, with elevated rates indicating emotional excitement during decision phases. These are often integrated in hybrid studies with fMRI or EEG; for example, combining eye-tracking with EEG identifies focused stimuli elements while correlating gaze data with neural patterns for comprehensive mapping. Such multimodal approaches enhance validity in by linking peripheral responses to central activity. Ethical considerations in neuroimaging for buyer decisions emphasize protection and , given the sensitive nature of neural data revealing subconscious preferences. Since the 2010s, regulations like the Neuromarketing Science and Business Association (NMSBA) Code of Ethics (2012) have mandated transparent disclosure of study purposes, voluntary participation, and to prevent misuse in commercial targeting. The EU's (GDPR, 2018) and California's Consumer Privacy Act (CPRA, 2023) further require explicit consent for biometric processing, addressing risks of unauthorized profiling. These frameworks ensure participant autonomy while mitigating concerns over mental erosion in experiments.

Key Insights from Neuromarketing

Neuromarketing research underscores that buyer decisions are predominantly influenced by emotional and processing rather than purely rational analysis. Studies from the early 2000s estimate that approximately 95% of all processes occur unconsciously and automatically, bypassing deliberate cognitive . This subconscious dominance is evidenced by activations in brain regions such as the , a core component of the reward circuitry, which correlates strongly with preferences for specific brands and products during purchasing evaluations. These findings imply that strategies emphasizing emotional , like evocative or sensory appeals, can more effectively sway consumers by aligning with innate neural pathways. A key insight from involves the interplay of reward anticipation and , rooted in , where neural responses to potential gains elicit robust signaling. release in areas like the ventral intensifies during exposure to perceived gains, such as product discounts, creating a heightened sense of pleasure and motivation that exceeds equivalent loss scenarios in terms of positive reinforcement. This asymmetry, where gains trigger stronger anticipatory rewards than losses do aversive signals, integrates with to explain why promotions and value propositions disproportionately drive impulsive buying. Marketers leverage this by framing offers to maximize perceived gains, thereby amplifying -driven engagement and purchase intent. Brand loyalty exhibits distinct neural signatures, particularly involving the (vmPFC), which activates during the valuation of trusted brands and contributes to their emotional premium in . A 2020 meta-analysis of fMRI studies confirmed vmPFC engagement as a hallmark of brand love and preference, linking it to subjective reward attribution that sustains long-term attachment. Complementing this, research from the 2020s highlights the basal ganglia's role in formation for brands, where repeated exposure strengthens automatic behavioral loops, transforming initial preferences into routine without conscious . These neural patterns suggest that building requires consistent of positive associations to embed brands in habitual neural circuits. Recent advances in from 2023 to 2025 have incorporated (VR) for immersive consumer testing, revealing amplified responses that deepen emotional bonds with brands. EEG studies during VR scenarios demonstrate greater mu rhythm suppression—indicative of heightened empathic engagement—compared to traditional media, allowing for more authentic simulation of purchase contexts. This approach enables marketers to refine campaigns by evoking stronger empathetic connections, such as through personalized narratives, ultimately enhancing decision influence in complex buying environments. Despite these insights, remains controversial. Critics argue that many studies suffer from small sample sizes, lack of reproducibility, and methodological flaws, leading to overhyped claims about predictive accuracy that often fail in real-world applications. Ethical debates extend beyond participant consent to concerns over manipulation through targeting, with some academics labeling the field as pseudoscientific or exploitative. As of 2025, ongoing reviews emphasize the need for rigorous standards to validate 's contributions to buyer decision research.

Applications and Variations

B2C versus B2B Processes

The buyer decision process in business-to-consumer (B2C) contexts is typically individual-driven, influenced heavily by emotional factors, and often quick for routine purchases such as groceries or everyday consumer goods, where low involvement predominates. For high-involvement purchases like durable goods (e.g., automobiles or ), consumers engage in more deliberate evaluation, but the process remains centered on personal needs, perceptions of value, and immediate gratification rather than long-term organizational impacts. In contrast, (B2B) processes are predominantly rational and involve multiple stakeholders within a buying center, comprising roles such as users, influencers, buyers, deciders, and gatekeepers who collectively shape the decision. These decisions feature longer cycles, often spanning months, and incorporate formal evaluations like requests for proposals (RFPs) to assess suppliers systematically, reflecting the structured nature of organizational . Key differences between B2C and B2B processes include the elevated risk in B2B scenarios, where poor choices can disrupt supply chains, incur significant financial losses, or affect across an entire organization, unlike the more contained personal risks in B2C. B2B evaluations emphasize metrics such as (TCO), which accounts for acquisition, maintenance, and disposal costs over time, whereas B2C focuses on perceived personal value, including emotional appeal and immediate . In the , B2B processes have evolved toward greater digital self-service, with 75% of B2B buyers preferring a rep-free experience and conducting much of their initial research independently online before engaging vendors, yet they retain committee-based approvals and consensus-building, distinguishing them from B2C's predominantly solo, impulsive decisions. This shift, accelerated by remote tools and data accessibility, streamlines initial stages but preserves the collaborative essence of organizational buying.

Impact of Digital Technologies

Digital technologies have profoundly transformed the buyer decision process by introducing new touchpoints that influence information search, evaluation, and purchase stages. Search engines serve as primary gateways, with mobile devices playing a central role; in 2025, smartphones drive more than 60% of purchases, underscoring how decisions increasingly begin with mobile searches. mechanisms, such as reviews and influencer endorsements, further shape perceptions, with 93% of consumers reporting that reviews impact their shopping choices. Influencers on platforms can positively affect purchasing decisions by building trust and aspiration through authentic-seeming content. AI-driven personalization has streamlined the process by reducing search time and enhancing relevance through recommendation engines. Amazon's collaborative filtering algorithm, which matches users based on similar purchase histories, accounts for approximately 35% of the platform's total sales by suggesting tailored products. Predictive analytics in these systems analyze browsing patterns to anticipate needs, thereby accelerating evaluation and increasing conversion rates without overwhelming users with irrelevant options. However, these advancements introduce challenges, including , which can impair decision quality by complicating the evaluation of options. Privacy concerns, amplified by regulations like the GDPR since 2018, lead 71% of consumers to cease business with companies mishandling data, fostering hesitation in sharing information essential for . Fake reviews exacerbate trust erosion, with 75% of consumers concerned about their prevalence, potentially misleading buyers toward inferior products. Emerging trends like shopping and (AR) try-ons are enhancing the evaluation phase by offering immersive experiences that bridge physical and digital gaps. Post-2022 studies indicate AR significantly boosts purchase intention by improving product visualization and engagement in e-commerce. In environments, fosters positive attitudes toward virtual shopping, though it varies by consumer familiarity. As of 2025, generative AI tools, such as chatbots, influence up to 40% of initial consumer queries in e-commerce, further personalizing the information search but raising additional considerations. Algorithmic biases in recommendation systems, however, can skew choices by reinforcing echo chambers or favoring certain demographics, as evidenced in analyses of AI-mediated consumer markets from 2023 onward.

References

  1. https://openstax.org/books/principles-marketing/pages/3-3-the-consumer-purchasing-decision-process
  2. https://pmc.ncbi.nlm.nih.gov/articles/PMC10006455/
  3. https://pdfs.semanticscholar.org/62b9/7cf6b0da37a4bc1ae322b226483e52747e74.pdf
  4. https://www.researchgate.net/publication/388937561_Understanding_the_Decision_Making_Process_in_Consumer_Buying_Behaviour_An_Insight
  5. https://www.sciencedirect.com/science/article/abs/pii/S0040162521005503
  6. https://journals.sagepub.com/doi/abs/10.1177/002224296502900408
  7. https://books.google.com/books/about/Consumer_Behavior.html?id=pqPOjwEACAAJ
  8. https://doi.org/10.1016/j.heliyon.2023.e13895
  9. https://courseware.cutm.ac.in/wp-content/uploads/2020/05/BUYING-BEHAVIOUR-Kotler-Armstrong.pdf
  10. https://link.springer.com/content/pdf/10.1057/palgrave.jt.5740193.pdf
  11. https://www.researchgate.net/publication/235253191_Problem_Recognition_The_Crucial_First_Stage_of_the_Consumer_Decision_Process
  12. https://ijhssm.org/issue_dcp/Relevance%20of%20Abraham%20Maslow%20s%20Theory%20to%20Contemporary%20Consumer%20Behavior.pdf
  13. https://books.google.com/books/about/Consumer_Behavior.html?id=Nk3x7wDYa5UC
  14. https://opentextbc.ca/introconsumerbehaviour/chapter/involvement-levels/
  15. https://www.researchgate.net/publication/259906084_Scarcity_messages_A_consumer_competition_perspective
  16. https://opentextbc.ca/introconsumerbehaviour/chapter/consumer-decision-making-process/
  17. https://faculty.ist.psu.edu/jjansen/academic/asist_bulletin_paid_search/02_chiang.pdf
  18. https://www.jstor.org/stable/2489245
  19. https://host.kelley.iu.edu/mwildenb/evolutionsearch.pdf
  20. https://datareportal.com/reports/digital-2025-sub-section-brand-discovery
  21. https://academic.oup.com/jcr/article/25/4/335/1785852
  22. https://business.columbia.edu/sites/default/files-efs/pubfiles/1175/compensatory_choice_models.pdf
  23. https://www.researchgate.net/publication/235361430_The_Theory_of_Buyer_Behavior
  24. http://bear.warrington.ufl.edu/brenner/mar7588/Papers/tversky-kahneman-science-1974.pdf
  25. https://www.jagsheth.com/wp-content/uploads/2015/12/Structural-Assumptions-Underlying-Fisbeins-Expectancy-Value-Model-of-Attitudes.pdf
  26. https://www.sciencedirect.com/science/article/pii/074959789190020T
  27. http://www.ijhssnet.com/journals/Vol_5_No_3_March_2015/24.pdf
  28. https://hbr.org/2023/01/research-how-price-changes-influence-consumers-buying-decisions
  29. https://www.psych.ualberta.ca/~msnyder/p486/read/files/R1987.pdf
  30. https://baymard.com/lists/cart-abandonment-rate
  31. https://hbr.org/1983/11/better-marketing-at-the-point-of-purchase
  32. https://link.springer.com/article/10.1007/s11002-023-09671-w
  33. https://www.sup.org/books/sociology/theory-cognitive-dissonance
  34. https://commons.erau.edu/cgi/viewcontent.cgi?article=1017&context=db-management
  35. https://www.europarl.europa.eu/topics/en/article/20201208STO93327/fast-fashion-eu-laws-for-sustainable-textile-consumption
  36. https://www.sciencedirect.com/science/article/pii/S2666784324000421
  37. https://link.springer.com/article/10.1007/s43621-025-00888-5
  38. https://opentextbc.ca/introconsumerbehaviour/chapter/the-perceptual-process/
  39. https://he.kendallhunt.com/sites/default/files/heupload/pdfs/Hanna_5e_Ch3.pdf
  40. https://myscp.onlinelibrary.wiley.com/doi/10.1016/j.jcps.2010.11.004
  41. https://pmc.ncbi.nlm.nih.gov/articles/PMC8359925/
  42. https://opentextbc.ca/introconsumerbehaviour/chapter/theories-on-personality/
  43. https://www.researchgate.net/publication/348963477_Application_of_Herzberg%27s_concepts_to_consumer_marketing_a_review
  44. https://opentextbc.ca/introconsumerbehaviour/chapter/behavioural-learning-theories/
  45. https://richardepetty.com/wp-content/uploads/2019/01/1986-advances-pettycacioppo.pdf
  46. https://pmc.ncbi.nlm.nih.gov/articles/PMC8962833/
  47. https://teddykw2.files.wordpress.com/2012/07/everett-m-rogers-diffusion-of-innovations.pdf
  48. https://www.sri.com/hoi/vals-market-research/
  49. https://www.jstor.org/stable/2489127
  50. https://people.duke.edu/~jrb12/bio/Jim/53.pdf
  51. https://www.jagsheth.com/wp-content/uploads/2014/02/a_theory_of_family_buying_decisions.pdf
  52. https://mariekedemooij.com/articles/demooij_2011_int_journal_cons_marketing.pdf
  53. https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?article=1014&context=orpc
  54. https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1696&context=senior_theses
  55. https://www.sciencedirect.com/science/article/abs/pii/S1090513810001455
  56. https://pmc.ncbi.nlm.nih.gov/articles/PMC7905187/
  57. https://openstax.org/books/principles-marketing/pages/3-2-factors-that-influence-consumer-buying-behavior
  58. https://journals.sagepub.com/doi/abs/10.1177/002224376600300403
  59. https://2012books.lardbucket.org/books/marketing-principles-v1.0/s06-02-situational-factors-that-affec.html
  60. https://www.jstor.org/stable/2489050
  61. https://link.springer.com/chapter/10.1007/978-94-011-1795-1_3
  62. https://opentextbc.ca/introconsumerbehaviour/chapter/situational-factors-and-influences/
  63. https://eprints.bournemouth.ac.uk/10107/1/Consumer_Behaviour_Theory_-_Approaches_%2526_Models.pdf
  64. https://dinastipub.org/DIJEMSS/article/download/2808/1843
  65. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.939403/full
  66. https://ijecm.co.uk/wp-content/uploads/2017/12/51220.pdf
  67. https://www.investopedia.com/terms/c/consumer-theory.asp
  68. https://dialnet.unirioja.es/descarga/articulo/5018489.pdf
  69. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-consumer-decision-journey
  70. https://www.thinkwithgoogle.com/_qs/documents/673/2011-winning-zmot-ebook_research-studies.pdf
  71. https://www.mdpi.com/0718-1876/20/1/21
  72. https://pmc.ncbi.nlm.nih.gov/articles/PMC11940918/
  73. https://www.sciencedirect.com/science/article/pii/S0167811624000648
  74. https://pmc.ncbi.nlm.nih.gov/articles/PMC2875927/
  75. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00515/full
  76. https://www.msi.org/wp-content/uploads/2021/11/MSI_Report_21-139.pdf
  77. https://doi.org/10.1016/j.neuron.2006.11.035
  78. https://pmc.ncbi.nlm.nih.gov/articles/PMC11153447/
  79. https://pmc.ncbi.nlm.nih.gov/articles/PMC10117197/
  80. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.665658/full
  81. https://www.frontiersin.org/journals/neuroergonomics/articles/10.3389/fnrgo.2021.672982/pdf
  82. https://link.springer.com/article/10.1007/s12152-025-09591-8
  83. https://www.nmsba.com/neuromarketing-companies/code-of-ethics
  84. https://www.sciencedirect.com/science/article/abs/pii/S0969698919315632
  85. https://bmcneurol.biomedcentral.com/articles/10.1186/1471-2377-13-13
  86. https://pmc.ncbi.nlm.nih.gov/articles/PMC9532451/
  87. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.534671/full
  88. https://www.researchgate.net/publication/391190359_The_Habit-Driven_Consumer_Behavioral_Insights_from_Neuromarketing_The_Habit-Driven_Consumer_Behavioral_Insights_from_Neuromarketing
  89. https://pmc.ncbi.nlm.nih.gov/articles/PMC11101072/
  90. https://www.researchgate.net/publication/389814074_The_Ethics_of_Neuromarketing_A_Rapid_Review
  91. https://theintactone.com/2025/09/04/criticisms-related-to-neuromarketing/
  92. https://www.mbaknol.com/marketing-management/the-engel-kollat-blackwell-model-of-consumer-behavior/
  93. https://www.researchgate.net/publication/282350425_An_old_model_for_a_new_age_Consumer_decision_making_in_participatory_digital_culture
  94. https://www.researchgate.net/publication/260248177_A_General_Model_for_Understanding_Organizational_Buying_Behavior
  95. https://opentext.wsu.edu/marketing/chapter/3-the-characteristics-of-business-to-business-b2b-markets/
  96. https://www.sciencedirect.com/science/article/abs/pii/S0019850123002560
  97. https://www.gartner.com/en/sales/insights/b2b-buying-journey
  98. https://www.forrester.com/blogs/self-service-buying-is-a-wake-up-call-for-b2b-sales/
  99. https://optinmonster.com/online-shopping-statistics/
  100. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.865702/full
  101. https://docs.rwu.edu/cgi/viewcontent.cgi?article=1157&context=nyscaproceedings
  102. https://www.amazon.science/the-history-of-amazons-recommendation-algorithm
  103. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1002698
  104. https://pmc.ncbi.nlm.nih.gov/articles/PMC8567038/
  105. https://www.quirks.com/articles/data-privacy-concerns-impact-consumer-purchasing-decisions
  106. https://www.bazaarvoice.com/press/bazaarvoice-study-reveals-consumers-concern-about-fake-reviews-63-think-brands-should-be-solving-the-issue/
  107. https://www.researchgate.net/publication/385255801_The_Impact_of_Augmented_Reality_in_Consumer_Behaviour
  108. https://onlinelibrary.wiley.com/doi/10.1002/cb.70027?af=R
  109. https://www2.deloitte.com/us/en/insights/industry/technology/digital-consumer-trends.html
  110. https://academic.oup.com/jla/article/15/1/1/7246686
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