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Five clusters of image for Icelandic energy
companies
About this publication
The publication was submitted and accepted as a conference paper at the International Academy of Business and Economics (IABE) Conference í Verona in June 2014. A slightly altered version, titled Elements of image
in the Icelandic energy sector, was presented at The European Institute of
Retailing and Services Studies in Bucharest in July 2014. Co-author is Dr. Thorhallur Gudlaugsson.
Abstract
Image is now an increasingly noteworthy issue in the electricity sector as a result of increased commercial competition following the liberalization of the electricity markets. This paper centres on measuring specific electricity image factors previously identified by qualitative research carried out to answer the research question: “Which elements are important for the image of electricity companies?”
The paper is based on a study conducted within the Icelandic electricity market where an online questionnaire was sent out to a convenience sample consisting of 278 electricity customers. The objective was to assess to what extent several identified elements were considered to be part of the image of three Icelandic electricity companies, and to show their relationship. These included trust, sustainability and service along with several other elements.
The paper maintains that, together with other researched elements, trust, sustainability and service elements are important, keeping in mind that they apply to the three companies to various degrees and can be categorised into five different clusters. The paper concludes that the energy companies should be attentive in addressing the identified issues as part of their image building.
Keywords: Electricity, Image, Sustainability, Trust, Perceptual
1. Introduction
To many consumers, electricity is no more than conveniently located wall sockets which turn light/heat/appliances on and off when needed. Limited attention is paid to the product itself as long as it is delivered with consistency and at the right price. Similarly, limited attention has been paid to branding and the brand image of electricity. This, however, has been changing as electricity retailers have begun to upsize their marketing budget, following the liberalization of electricity markets. Consequently, both private households and businesses are now able to choose between the various companies supplying electricity. Thus it can be stated that the industry has switched from a distribution environment to a competitive one. This gives the consumer added influence and requires the companies to look more closely at their image. The research addresses the above requirement by measuring which elements are important to corporate image, hence the research question:
“Which elements are important for the image of electricity companies?”
It should be noted, however, that establishing what to measure may be particularly challenging in the electricity sector, as a review of the liberalization of the electricity markets reveals several factors which may affect the branding strategies of electricity providers, and are specific to that particular industry. They are: Politics, ideology and necessity, complexity of markets, the intangible nature of electricity, and interconnectedness of attitudes with both politics and electricity reforms (Larsen, 2014).
2. Theoretical Background
Brand image can be said to be a new variety of longstanding psychological variables that aim to rationalize consumer behaviour (Hsieh, Pan, & Setiono, 2004). Brand image is an extrinsic attribute associated with a product but not an actual part of it (Fandos & Flavián, 2006), and brand image affects consumers’ personal judgment about the brand’s overall fineness or superiority (Yoo, Donthu, & Lee, 2000). Through brand image, consumers are able to identify a product, assess its quality, lower purchase
risks, and attain experience and satisfaction out of product differentiation (Lin & Lin, 2007).
The definitions and operationalization of brand image have been rather irregular, though these share some patterns and commonalities. In a summary of definitions and conceptualizations of brand image from 28 studies on the history of marketing literature, Dobni and Zinkhan (1990) offer a consolidated collection of definitions based on the authors’ principal areas of emphasis. They define brand image as the reasoned or emotional perceptions consumers attach to specific brands. However, brand image is such a multi-dimensional concept that authors have no consensus of how to empirically measure it (Hsieh, 2002; Martinez & de Chernatony, 2004; Randheer, AL-Motawa, & Khan, 2012; Stern, Zinkhan, & Jaju, 2001) and the past generation has witnessed the use of multiple tools, routines, and methods to examine its content and organization (Stern et al., 2001). Furthermore, image measures are industry specific. Randheer (2012), for example, endeavoured to formalize a brand image structure with three dimensions via value, quality and awareness and resolved that while those dimensions are connected and can impact the image of a the brand, they are not complete sets in themselves. Amid explanations for various and often incommensurable methodological techniques we find wide differences in the conceptualization of components to be measured; paradigmatic differences about the value of specific types of data; and controversy about the manner in which data are collected, classified, coded, analysed, and represented (Stern et al., 2001). Thus, diverse authors use divergent tactics to measure brand image. Keller (1993) measures brand image by brand associations related to the product, favourability of brand associations, their strength, and their uniqueness. Aaker (1996), on the other hand, proposes that brand image be assessed through association/differentiation measures regarding value, brand personality, organizational associations, and differentiation. Within the theme brand equity, Lasser et al. (1995) developed a scale for measuring consumer-based brand equity, in which they refer to it as a social image.
Industry-specific research on image in the electricity industry is scarce. Furthermore, building a brand image for a commodity product like electricity is challenging since there is no actual distinction in the product itself. Thus electricity from one supplier is identical to electricity from another. However, research confirms that people are less likely to buy electricity from a supplier they have never heard of (Stanton et al., 2001). Larsen (2012) studied brand image in qualitative research in two markets,
Iceland and Poland, and found that several elements were considered to represent an important aspect of image; that is, Country of origin, Green factors, Imagery, Service, Sustainability and Trust. Those were tested for the purposes of this study by means of quantitative research and based on a survey done in December 2013. A more detailed account of the research and the methodology used is found in the next chapter.
3. METHODOLOGY
The methodology chapter outlines the questionnaire used and describes population and procedures. The survey commenced in November 2013 and ended in December 2013; at which time 278 had participated. When data gathering was complete it was imported into SPSS.
First, average attribute scores for the three companies were calculated and compared. Then the companies’ relative image was calculated using the perceptual mapping software designed by Lilian and Rangaswamy (2003)
3.1 Questionnaire and data analysis
A questionnaire was constructed to measure quantitatively the extent to which the elements in the above mentioned research by Larsen (2012) in fact constitute an important aspect of image. Further factors were added from Geirsdottir and Larsen (2013) who measured the image of Icelandic energy companies based on Keller’s (2001) CBBE model. Additional elements were added on the basis of previous image studies by authors.
In the first section, participants were asked to select the electricity company that sprang first to mind, and in the second, they were asked to evaluate three Icelandic electricity companies (Orkuveita Reykjavíkur, <OR> Orkusalan, <OS> and HS-Orka <HS>) according to a set of given attributes. The attributes were: “Honesty”, “Trust”, “Reliability”, “Social responsibility”, “Progressive”, “Modern”, “Appeals to me”, “Sustainability”, “Old-fashioned”, “Corruption”, “Good services”, and “Favourable price”. The participants evaluated each company on each attribute using a nine point scale, where 1=applies very badly to this company or brand, and 9=applies highly accurately to this company or brand. In this research the companies’ equal goods and attributes constitute the image attributes listed above. When evaluating the internal consistency reliability, the Cronbach’s Alpha for OR was 0.886, for OS 0.816 and for HS it was 0.805. Values above 0.7 are considered acceptable
but values above 0.8 are preferable. In this study, all cases have values above 0.8 and, therefore, the image attribute scale is considered to have good internal consistency.
One of the more sophisticated research methods used in marketing and business science is perceptual mapping. Normally, this shows how goods in a market are perceived based on certain attributes, and which goods are competitors as seen from the customers’ point of view. These maps, therefore, can be of great help when management related decisions have to be made (Festervand, 2000; Festervand, 2002; Kara, Kaynak and Kucukemiroglu, 1996; Stanton and Lowenhar, 1977). Figure 1 shows a hypothetical perceptual map used to explain how this works and how to interpret results.
FIGURE 1: HYPOTHETICAL PERCEPTUAL MAP
The map shows four products (in this research, companies) that are evaluated based on five attributes which can be both positive and negative. When choosing attributes it is important to select those that describe both the industry and individual goods. Various methods can be used to identify the attributes. It is common practice to start with many attributes and then, using the methodology, combine them and/or narrow down the selection. The research reported here uses positioning analysis software developed by Lilien and Rangaswamy (2003). The results are shown in a vector format. The software positions the vectors and determines their length
based on the average scores for each good´s attributes. Many similar methods exist (Gwin, 2003; Sharp and Romaniuk, 2000; Bijmolt and Wedel, 1999; Sinclair and Stalling, 1990; Kohli and Leuthesser, 1993; Shugan, 2004).
The length of the vectors indicates how well or decisively attributes can distinguish between the products. A long vector indicates that the attribute is decisive in the participant’s mind. The further the product is from the centre of the map the more decisive is its differentiation based on that attribute. It is important to keep in mind that the vectors are read in both directions from the centre of the map even though only one of the vectors is shown (Lilien and Rangaswamy, 2003). For example it can be seen that product 1 is less connected to attribute 4 than are the other products. The size of the angle between the vectors also gives important information. An acute angle indicates that the attributes are closely related since the correlation between them is high.
Participants were also asked to indicate from which company they buy their electricity, how likely they are to switch to another company in the next six months, their gender and age.
4. RESULTS
In this section the results of research are outlined, starting with a description of the participants. Next, average attribute scores for the companies are reported, followed by a presentation of a perceptual map, which is then further interpreted.
4.1 Participants
The majority of participants were female, or just over 63%. Since image might differ between genders, the answers from males were weighted equal to the total answers from women. Following this procedure, the total number of answers was 350 and all other analyses were based on that amount of data. 22% of the participants were 20 years of age or younger; 35% were between the ages of 20-24; 11% were between the ages of 25-29; 15% were between the ages of 30-39; and 17% were aged 40 or older. The majority were customers of Orkuveita Reykjavikur, or 65%; 4.7% were customers of Orkusalan; 3.3% of HS-orka; and 2.9% of other energy companies. It is of interest to note that 24% of the participants did not know from which company their household bought electricity.
4.2 Average attribute scores
In this section, average attribute scores are presented. Figure 2 shows the results between the three energy companies. As can be seen, the values differ between companies and in most cases the difference is statistically significant (95%). Orkusalan (OS) has a higher score for honesty (5.96) than HS-orka (HS) which has 5.42, and both Orkuveita Reykjavikur (OR, 5.94) and OS (5.87) have higher scores than HS (5.42). OR (6.60) have a higher score for reliability than HS (5.89), and both OR (5.77) and OS (5.68) have higher scores than HS (5.26). When looking at the image attribute “progressive”, OS (6.39) have a higher score than both OR (5.75) and HS (5.43). This is also the case for the image attribute “modern”, where OS (6.61) have a higher score than both OR (5.73) and HS (5.62). When looking at the image attribute “appeals to me” both OR (5.41) and OS (5.32) have higher scores than HS (4.49). OR (5.46) have a higher score for “old-fashioned” than both HS (4.86) and OS (3.86) and HS have a higher score than OS.
FIGURE 2: COMPARISON OF AVERAGE SCORES BETWEEN COMPANIES.
In the image attribute “corruption” OR (5.98) have the highest score, significantly above the scores of both HS (5.39) and OS (4.49) for that attribute. OR (5.97) have a higher score for the image attribute “good services” than HS (5.52). As one can see from this discussion, there was always a statistically significant difference between companies based on image attributes, with the exception of two; “sustainability” and “favourable price”. In some cases OR received the highest score and sometimes OS came out on top. HS never gained the highest score and in fact in most cases received the lowest score compared to the other two.
It is important to keep in mind that some image attributes are positive and some are negative in nature and it is not clear on which image attributes the companies differ from one another. To evaluate this, perceptual mapping is useful.
1,00 2,00 3,00 4,00 5,00 6,00 7,00 Honesty Trust Reliability Social responsibility Progressive Modern Appeals to me Sustainability Old-‐fashioned Corruption Good services Favorable price HS OS OR
4.3 Differentiation based on perceptual mapping
Figure 3 shows the position map for those three companies based on twelve image attributes (see figure 2). Perceptual maps show the attributes as vectors, which should be read in both directions. The representation in figure 3 indicates that the findings are robust since the attributes “modern” and “old-fashioned” have opposite directions. The same applies to the attributes “honesty” and “corruption”. Attributes that obviously have something in common should also be together. This is the case with the attributes “trust” and “social responsibility” and “modern” and “sustainability”. In fact, from figure 3 it can be seen that the image attribute is arranged in five clusters (C1, C2, C3, C4 and C5).
FIGURE 3: PERCEPTUAL MAP FOR THREE ENERGY COMPANIES Honesty Favorable price Progressive Sustainability Modern Reliability Good service Social responsibility Trust Appeals to me Corruption Old-‐fashioned
HS
OS
OR
C1 C2 C3 C4 C5In C1 there are two image attributes, “corruption” and “old-fashioned”.
Since corruption is obviously negative it seems that being old-fashioned, at least in this case, is also negative because those two lie side by side on the map. In the opposite direction are two clusters. C2 has two image
attributes, “modern” and “sustainability” and C3 has three image attributes,
“honesty”, “progressive” and “favourable price”. C2 and C3 lie side by side
on the map which indicates that those five attributes share something positive and are located opposite C1, the negative cluster.
C4 has four image attributes, “good services”, “social responsibility”,
“trust” and “appeals to me” and lie between C1, on the one hand, and C2
and C3, on the other. C5 is in fact not a cluster since it comprises only one
image attribute, “reliability”, but it is connected to C4.
As figure 3 shows, the companies have different locations on the map, which indicates that they also occupy different positions in the participants’ minds. Orkuveita Reykjavikur (OR) is located in the northern part of the map. Judging by its distance from the centre and from other companies’ locations on the map, its position in the minds of the participants seems relatively distinct and clear. OR is most closely connected to the positive attribute “reliability”, but it is also linked to the negative C1 “corruption and old-fashioned”, which is negative for the
company. Orkusalan (OS) is located in the south-eastern area of the map. As is the case with OR, the positions seem relatively distinct and clear since its location is far from the centre and distant from the other companies. OS is the company most closely connected to the attributes “modern” and “sustainability” (C2) and the attributes “honesty”,
“progressive” and “favourable price” (C3). OS is also least connected to
the attributes “corruption” and “old-fashioned” (C1). It, therefore, has a
highly positive image, since both clusters (C2 and C3) are positive.
HS-Orka (HS) is located in the south-western part of the map. As is the case with OR and OS the position seems to be relatively distinct and clear since its location is far from the centre and away from the other companies. HS does not, however, have a direct connection to any of the attributes. It is the company that is least connected to the attributes “good services”, “social responsibility”, “trust” and “appeals to me”, (C4) which is
negative, as well as to the attribute “reliability” (C5). Since both those
clusters are positive it is concluded that, compared to OR and OS, HS has a negative image.
5. Discussion
The paper has addressed the image of electricity companies by answering the research question, “Which elements are important for the image of electricity companies?” Findings show that, to various degrees, all the identified elements apply to the chosen companies although it is difficult to generalize as to which elements are most important to consumers, (e.g. price, trust, service etc.). However, by drawing up a perceptual map, it can be shown that the participants perceive the three companies researched very differently. HS does not appear to have any differentiating attributes associated with it, whereas the other two do. OR is considered reliable, socially responsible, and trustworthy. It is, furthermore, considered to be appealing and providing good service. OS is considered modern, sustainable, progressive, honest and offering a favourable price. Those results are important from a practical perspective. It is also interesting, from both a practical and academic perspective, to note that some image attributes can be clustered together, since they are seen as related by participants, and they differentiate companies offering a homogeneous product such as electricity. However, further research would be needed to truly assess how important a role these attributes play in creating the image of electricity companies. This would ideally be undertaken by the construction of a preference map. Furthermore, future research is needed, using a more reliable sample. Although the sample used in this research is adequate, it is neither large enough, nor a sufficiently random sample of the population to be statistically reliable.
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About authors:
Dr. Fridrik Larsen ([email protected]) is an assistant professor of Business
Administration at the University of Iceland, School of Business. His main research focus is on energy branding and destination branding. He is the head of the Icelandic Marketing Association (IMARK) and an international branding consultant.
Dr. Thorhallur Gudlaugsson ([email protected]) is an associate professor of
Business Administration at the University of Iceland, School of Business. His main research focus is in market orientation, service quality, service management and branding.
