Understanding Affective Experiences: Towards a Practical Framework in the VALIT-Project | Mika Boedeker


TAMKjournal | In the b2b-sector the role of affective experiences has not been as salient as in the b2c-sector. However, it would be deceptive to expect that people leave all their emotions, feelings etc. behind when they are in some role in the b2b-encounters. People in b2b-encounters do have affective experiences while doing business with each other, and those experiences, in many cases some kind of “affective path”, should be taken into account and managed to enhance value perception and value creation. But how to measure affective experiences in a sufficient depth and precision but at the same time with a reasonable control over the instrument? This article takes some steps towards that contribution by presenting a tentative framework, which combines two basic approaches dealing with affective experiences.

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The experiential and affective aspects have already for a long time been included in various studies of consumer behavior. A vast amount of related studies has been done and there is no doubt of the relevance of the issue in the b2c-sector.

In the b2b-sector the role of affective experiences has not been as salient as in the b2c-sector (e.g. Tähtinen & Blois 2010). However, as for example Tähtinen and Blois (2010) point out, it would be deceptive to expect that people who are emotional consumers, leave all their emotions behind when they are in some role in the b2b-encounters (see also e.g. Knight 2012). People in b2b-encounters do have affective experiences while doing business with each other, and those experiences, in many cases some kind of “affective path”, should be taken into account and managed to enhance value perception and value creation. This is one of the key-issues in the so called VALIT (www.valit.fi) R&D-project (Tekes-funded R&D-project of Tampere University of Applied Sciences, Tampere University of Technology, University of Vaasa and VTT Research Centre).

Though important, detecting affective experiences and turning this knowledge into managerial implications isn’t that straightforward. The purpose of this article is to take some steps towards that contribution. First, the concept of affective phenomena in general is discussed. After that two basic approaches dealing with affective experiences are introduced followed by ideas of combining these approaches in one framework. Finally, three preliminary applications in the VALIT R&D-project are shortly described.

Concept of affective phenomena

The use of the terms of various affective phenomena (“emotion”, “feeling”, “mood”, preference”, “attitude”, “emotional”, “affective” etc.) both in scientific and in folk concepts is fuzzy and confusing (e.g. Bagozzi, Gopinath & Nyer 1999; Kokkonen 2010; Scherer 2005). For example Scherer (2005) points out, that counting the number of scientific definitions of emotion is hopeless while on the other hand, there is no answer to the question of the number of emotions. Various approaches have been presented, for example ranging from a limited amount of basic dimensions or basic categories to a vast number of discrete or specific affective terms.

How to detect affective experiences in a sufficient depth and precision but at the same time with a reasonable control over the instrument?

In that situation it is crucial to clarify how the terms or concepts are used in each case. The definition of terms offers an interpretation of the terms but equally important is how the variables to which the terms refer are later operationalized (Bagozzi, Gopinath & Nyer 1999). Experience refers to a process of living through an event (Erlebnis, elämys) rather than to knowledge and expertise (Erfahrung, kokemus) (Palmer 2010). Affective experience refers especially to the affective dimension of experience, which involves one’s affective system through the generation of emotions, feelings and moods (Gentile, Spiller & Noci 2007). In that sense affect is conceived as an umbrella. In turn, and when necessary, emotions, feelings and moods can be distinguished according to their features of intensity, duration, cause, awareness, control etc. (e.g. Bagozzi, Gopinath & Nyer 1999; Beedie, Terry & Lane 2005; Derbaix & Pham 1991; Kokkonen 2010; Scherer 2005).

Dimensional approach and the PAD-model

There are several dimensional presentations of affective experiences, most of them focusing on the dimensions of valence and arousal resulting in a circumplex structure (see e.g. Seo, Feldman Barret & Jin 2008). Mehrabian & Russell (1974) suggested that there exists a limited set of basic affective responses to all stimulus situations, independent of the sensory modality involved and this framework has been applied in numerous studies ever since. Variations in pleasure (valence), arousal and dominance (PAD) should universally constitute the common core of human affective states. Put together, these form a three-dimensional affective space (Figure 1).

FIGURE 1. Affective space of PAD
FIGURE 1  Affective space of PAD


In a general level, pleasure refers to the degree to which an individual feels for example good, happy, or satisfied. Similarly, arousal refers to the degree to which an individual feels for example excited, stimulated, alert, or active. Dominance, in turn, refers to the degree to which an individual feels for example in control of, or free to act (e.g. Donovan & Rossiter 1982; Mehrabian & Russell 1974).

Hierarchical approach

As with the dimensional approach, there are several presentations of various specific affect categories (see e.g. Laros & Steenkamp 2005). Laros & Steenkamp (2005) presented an approach to combine the general dimensions of positive and negative affect to more specific affective experiences (Table 1). In their model the general dimensions form the superordinate level. The general dimensions are further divided in the intermediate level to a limited number of eight basic affective experiences. These in turn include altogether 41 specific affective experiences in the subordinate level based on their extensive literature review.

TABLE 1 Hierarchy of affective experiences (adapted from Laros & Steenkamp 2005)

Mka TABLE1.1

Among other things, the results of their study suggest that the intermediate (basic) level provides more information about the affective experiences of the consumer over and above the superordinate (general) level (Laros & Steenkamp 2005).

Towards a combined approach

So, how to grasp affective experiences in such manner, that they are detected in a sufficient depth and precision but at the same time with a reasonable control over the instrument?

Dimensional format is convenient because answers to only two or three general dimensions, rather than multiple specific affective terms, are indicated. On the other hand, this format may be difficult because the dimensions are rather abstract and do not always correspond to the way one naturally talks about affective experiences (Sacharin, Schlegel & Scherer 2012). Basic level provides more information over and above general dimensions (Laros & Steenkamp 2005) and specific affective terms correspond to the natural way of talking about affective experiences (Sacharin, Schlegel & Scherer 2012).

However, it may be misleading to treat self-reports of sadness, fear, anger, and the like, as discrete entities in the face of evidence that some individual’s do not report them as discrete. Some individuals represent their experiences with a good deal of precision (high granularity), whereas others represent their experiences in more global terms (low granularity) (Feldman Barret 1998, 2004; Scherer 2005). Thus, a practical framework to detect the affective experiences in various degrees of granularity is needed.

In the Figure 2 the aforementioned three-dimensional PAD-space is presented in a two-dimensional pie-type graphic so, that each of the four pleasure-arousal combinations (elation, serenity, lethargy and tension; see e.g. Seo, Feldman Barret & Jin 2008) are further divided according to dominance to form eight “affective families.

FIGURE 2 A tentative framework of affective families

In this way more than just the general dimensions of positive and negative affect, pleasure and displeasure etc. can be detected. Additionally, to make the framework correspond to the natural way of talking about affective experiences, the affective families need to be characterized by selected specific affective terms. The affective terms provided here represent a larger affective family and thus refer to a whole range of similar kind of terms. The terms are selected based on circumplex-type presentations like for example in Mehrabian & Russell (1974), Seo, Feldman Barret & Jin (2008) and Scherer (2005), and on lists of affective terms like for example in Laros & Steenkam (2005) and Meek (2011). Finally, the underlying dimensional structure is important to locate the affective experiences to increase their manageability.

When using the framework for empirical research purposes, the researcher has to decide, whether or not to make a distinction between emotions, feelings and moods and on which criteria (various criteria can be found for example in Bagozzi, Gopinath & Nyer (1999); Beedie, Terry & Lane (2005); Derbaix & Pham 1991; Kokkonen (2010) and Scherer (2005). Other forms of distinction might be relevant as well, for example if the experience is of social nature, for example pride, empathy, shame and embarrassment (e.g. Bagozzi 2006, Tähtinen & Blois 2010). For some purposes it is important to make the difference, because the difference may manifest itself as distinct causes or consequences of the experiences and therefore be sensitive to different managerial interventions. Additionally, the researcher has to decide if detecting the affective family is enough or if more precise distinction within the affective family is needed.

Some preliminary applications

The framework has been applied so far in three early-stage VALIT-studies. In the first study, b2b-respondents were asked in a questionnaire to evaluate their level of pleasure, arousal and dominance related to episodes in certain business encounters. The work continued in the second study by developing the framework further as a more illustrative graphic tool to assist in the subsequent personal interviews in that particular study and also for other future purposes. In the third study, Twitter data in a b2b-context was analyzed initially by their sentiment in terms of positive-neutral-negative and subsequently in a more precise manner by affective families with the help of the framework. The work continues also in this and other social media context.


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About the author

Mika Boedeker
Principal Lecturer, Ph.D.(Econ. & Bus. Adm.)
School of Business and Services
Tampere University of Applied Sciences
Specialties: Research methodology, service business and consumer behavior.