road landscape as a product: does It satIsfy consumers’ aesthetIc needs?

inga.stankevice@ktu.edu abstract. The contemporary market laws and consumer society impose that road users are regarded to as consumers, and road landscape – as a product. In this context, consumers’ willingness to use the product is equivalent to road users’ willingness to drive on the road. Consumers’ fastidiousness induces to develop road landscape in a way which ensures satisfaction of drivers’ versatile needs, including the aesthetic ones. However, development of desirable-to-drive road landscape remains one of the challenges of land management. Hence, based on the analysis of aesthetic needs of road users, the paper proposes a framework for creation of desirable-to-drive road landscape. The research rests on photo-fixation, Kansei engineering, regression, correlation and descriptive analyses. The landscape of the main Lithuanian roads was investigated. The results show that the willingness to drive on a road depends on the level of tune of all the elements comprising the roadscape, the amount of positive impression left by the roadscape, roadscape’s pleasurability, level of sophistication, and skittishness. The guidelines for the development of desirable to drive road landscape are proposed, and the map of distribution of roadscape in Lithuania, according to the willingness to drive, is


Introduction
Mobility is a social spatial habit which is closely related to the respective cultural norms. Lynch (1981) distinguishes two modes of mobility: "pleasurable drive" in a beautiful landscape which can also become a good means of spending leisure time; and "just drive" which is based on necessity to move from point A to point B and which, according to the scholar, is a boring and futile waste of time. The motion of pedestrians and vehicles is treated as not only a physical action, but as the formation of social bonds and identity. Nowadays, this understanding has even lead to the emergence of a new concept -landscape services. They are defined as "contributions of landscapes and landscape elements to human well-being" (Bastian et al. 2014).
The specific culture of auto-tourism was born in the beginning of the 20 th century in the USA. However, the observation, evaluation and formation of road landscape from the aesthetic viewpoint of a consumer on the scientific level began in the second half of the 20 th century. Nowadays, the USA, Germany, Great Britain, Australia are the leaders in formation of an aesthetic and pleasurable road landscape. These countries have developed evaluation methods for the roadscape, they have road landscape guidelines and implemented programmes for beautifying this kind of landscape. In Lithuania, Landscape design guidelines for the state roads and railways have been prepared and published in the end of 2013 (ordered by the Ministry of Environment). Thus, decisions of locals and tourists to drive on a road are more than a conscious decisions expressed in terms of "if…, then…" rules. Attractive roads are chosen based on not only aspects of structure, territory, traffic and network characteristics of the roads (Augeri et al. 2014), but on "non-graspable, sometimes inexplicable internal atmospheric experiences" (Kasemets 2014) as well.
Despite the relevance of the topic, creation of an aesthetic road landscape which, in turn, satisfies road users, remains one of the challenges of sustainable land management and tourism. Aesthetics is a measure of visual perception of environment. In this paper, road landscape is treated as a product. Road users, i.e. drivers and passengers, are the main consumers of the product. Therefore, the focus of this research is to identify what kind of road landscape as a product would cause consumers' willingness to use it. Willingness to use a roadscape is transformed in the research into willingness to drive on a road. road landscape as a product: does It satIsfy consumers' aesthetIc needs?

Irina matijošaitienė 1 , Inga stankevičė 2
Now, it is important to consider what value of a roadscape means for a contemporary consumer. Nowadays, consumers are characterized by a number of relatively new features: they treasure experience, they are ingenious and demanding, they treasure time and are strenuous (Santala, Parvinen 2007). Contemporary consumers are viewed as generalized consumers, who, according to Daub and Ergenzinger (2005), "can be satisfied only by products and services that have no harmful impact on any of the areas in which they live or operate", and who expect to be satisfied by the receivable value in every aspect of their activities, including driving. It is widely acknowledged that during the planning process of a roadscape, social, cultural and consumer values are negotiated and reframed in the context of the road landscape (Liu, Opdam 2014).
Consumer value has been analyzed since the 1980s, and its comprehension, due to the change of the perception of consumers, has also elaborated to a great extent (Fig. 1).
Based on Fig. 1, which integrates the research results of a number of scholars (Chang, Wang 2011;Flint et al. 2008;Khalifa 2004;Martelo Landroguez et al. 2011;Mele 2007;O'Sullivan, McCallig 2012;Rintamäki et al. 2007;Ryu et al. 2010;Strandvik et al. 2012), following conclusions were made: − consumer value is a positive significance of a product to a consumer; − consumer value is perceived in the context of alternatives of the product; − consumer perceived value encompasses a number of dimensions, which are conditionally divided into benefit and cost; − recent perception of consumer value is based not on the contrast of benefit and cost but rather on their reconciliation; − because consumers are different and not always rational, the shift from value calculation to its identification, creation and communication occurs. Thus, consumer value is a multidimensional construct, agreeably to Grönroos and Helle (2010), and Santala and Parvinen (2007). Similarly, Dallos (2013)  According to Chvilickas and Smaliukienė (2009), highly motivated consumers tend to rely on cognitive components, whereas individuals with low motivation use emotional aspects to form their attitudes. Because consumers of a certain road usually have low intrinsic motivation to use it -rather, they are forced by extrinsic factors to do so (Lynch 1981), their perceived value and satisfaction are dependent on emotional aspects, such as, for example, interactive experience, roused emotions (Bastian et al. 2014;Holbrook 2005;Kasemets 2014;Liu, Opdam 2014) and wish to drive on a road (Flint et al. 2008). However, wish to use a certain product depends on previous experience and previous emotions (Mascarenhas et al. 2004), therefore willingness as a formative emotional element of consumer value is regarded as a higher-level expression of consumer value. Hence, in light of the presented definitions of consumer value of a road landscape, the value is defined as a road user's willingness to drive on that road. fig. 1. Dynamics of definitions of consumer value (Stankevičė 2004) However, the analysis of the literature revealed the lack of evaluation of road landscape as a product. The need for analyzing road landscape from the perspective of an aesthetic consumer is determined by conscious consumer society and its willingness to use the best, safest, most comfortable and pleasurable product. Thus, the paper is aimed at proposing a framework of development of desirable-todrive road landscape from the aesthetic viewpoint of a consumer. The research methodology rests on field research, which includes observation on site with purposive photofixation, quantitative survey with the application of the idea of Kansei engineering method, semantic differential scale and affinity analysis. Then, multiple linear regression and correlation analyses, as well as descriptive analysis and descriptive statistics are used to investigate the data.

observation on-site
Observation of road landscape on-site includes driving on each road and photo-fixation of characteristic views and panoramas. To identify the particular places of the photofixation, the employed observation method relied on peculiarities of landscape perception which are presented by Cullen (1995). There are three expositional zones of anthropogenic objects: − predominance of scale (up to 3 h, where h is an object's height); − predominance of scenery (up to 3.5 km); − the zone of psychological effect (up to 6 km). The level of coverage of the zone of predominance of scale is too low and, consequently, improper for taking pictures. On the contrary, the zone of psychological effect embraces large areas: even though distant objects could be seen, they become entirely impersonal. In the zone of predominance of scenery objects which are situated beyond the respective boundaries are perceived as an unclear background, and objects which are situated within the boundaries are clearly seen. Therefore, the landscape was photographed as close as each 3.5 km at least.
However, the distance fits the research if the corresponding road is built on a flat landscape and if there is a considerable visual space around it. Otherwise, the distance must be shortened in places with altering landscape, where a prominent dominant is inserted. In accordance with the principles of serial vision, the space can be divided into "here" and "there" (Cullen 1995). Based on the above developed peculiarities of the perception of landscape, places of the photo-fixation of the road landscape are identified: a) if a route is straight and a road is located in a flat landscapeas often as each 3.5 km at least, b) in the places of alteration of landscape, c) when a prominent dominant appears within a field of view of a landscape, d) after a turn or on the top of a hill when one merges into another space, or "there".
The pictures were taken on days with similar weather conditions. Moreover, several photo-shots of the same landscape helped trace the sceneries. It was purposive because a focusing angle of a camera is narrower than a field of human sight, and several photo-shots can thus be integrated into a one-piece picture which reflects the whole scenery of a landscape. This technique was applied to a mixed landscape. In total, 1505 pictures were made. However, only the pictures which best reflected the sceneries (288) were chosen for presenting them in the survey.
Besides, as different weather conditions do, different seasons affect the comprehension of a roadscape as well. One of the examples proving this fact is Drottenborg's (2002) experiment when she researched driving safety in the same landscape before and after blossom of wild cherries. According to her, more beautiful landscape during blossom caused about 5% decreased driving speed. This experiment demonstrates that different seasons do affect the comprehension of landscape. Yet an assessment of hedonomics of the sample roadscapes during different seasons and the subsequent comparison of the findings necessitates a separate extensive study and exceeds the scope of this paper. Hence, this issue is left for further research.

Quantitative survey
The selected pictures became a part of the questionnaire of the quantitative survey. Though the questionnaire includes demographic questions, its main part is composed of the numbered pictures, and the lists of evaluation criteria below them. The choice of the criteria is based on the idea of Kansei engineering method. Recently, the method has been used in an early stage of creation of a product in order to make the product pleasurable. Kansei engineering method enables to measure perception and link it to design, beauty and aesthetics criteria (Hartono, Chuan 2011). With reference to the method (Llinares, Page 2008), 123 words and phrases, describing road landscape, from various scientific literature were distinguished. Later affinity analysis was applied, and a group of 10 students participated in the process. They grouped 123 words and phrases into 28 groups. This resulted in 28 words and phrases, to drive on this road. It is important to emphasize that the latter criterion indicates the level of satisfaction experienced by an aesthetic consumer. Scales of semantic differential with five levels of gradation were provided in order to get measurements of each of the pictures. Circles were assigned to the values: the smallest circles meant the smallest approval, and the biggest circles meant the greatest approval. The sequence of the circles varied from line to line, and the sequence of the distinguished pairs was mixed from picture to picture. This was done in order to keep the respondents thinking and prevent them from giving automatic responses.
The questionnaires were placed in a Lithuanian portal of internet-based surveys and were accessible to all who wanted to participate in the survey or had an electronic link to the survey. The total number of respondents reached N = 249. The data were analysed by PASW Statistics 17.0 and became subject to regression and correlation analyses. Finally, the results of the analyses and the relevant pictures were analysed integrally. Then the guidelines for the development of desirable to drive roadscape were proposed. This stage of the methodology rests on the brief descriptive analysis. Descriptive statistics method was used for the identification of the most and least desirable to drive road landscape in Lithuania.

Identification of factors affecting willingness to drive
Willingness to drive on a road is presented here through the variable Would like to drive. First of all, it was identified on which variables and how strongly the variable Would like to drive depended. Eight models of regression equations were proposed. With reference to the coefficients' and ANOVA tables, all the β-coefficients, except for constants in the 2 nd (p = 0.857 > α), 3 rd (p = 0.209 > α) and the 4 th (p = 0.079 > α) models, were statistically significant (p = [0.000; 0.045] < α = 0.05). The partial correlation coefficients showed that only the 1 st − 5 th models had no coefficients which do not improve the equation statistically significantly. Based on the findings above, only the 1st and the 5 th models are selected for further analysis.
According to the table of model summary, the 5 th model has a bigger adjusted determination coefficients r 2 adj = 0.602 and a smaller standardized error of the estimate S 2 e = 0.5528 in comparison to the 1 st model (r 2 adj = 0.431, S 2 e = 0.6605), therefore the 5 th model was selected for the analysis.
The linearity of the regression equation was confirmed (according to ANOVA table, p = 0.000 < α = 0.05), and the hypothesis about the equality of the coefficients to zero was denied (p = 0.000 < α = 0.05). The dispersion of the residuals was analysed through a scatter-plot and appeared to be constant. The normality of distribution of the residuals was verified by histogram, normal P-P plot and Kolmogorov-Smirnov test (p = 0.328 > α = 0.05), and appeared to be consistent with normal distribution. The problem of multicollinearity was absent in the model because all the variance inflation factors (VIF) for multicollinearity were below 4 (VIF β1 = 1.679, VIF β2 = 1.623, VIF β3 = 1.675, VIF β4 = 1.660, VIF β5 = 1.404). Autocorrelation was absent, which was confirmed by Dubrin-Watson statistical value 1.950 ~ 2. There were no outliers in the data because moduli of all the standardized residuals were <3.5.
Thus, the regression equation is suitable for making forecasts: With reference to the equation and the confidence intervals for β coefficients, the forecast with a 95% guarantee was made: − if tune of elements of a road landscape increases by 1 conditional unit, the willingness to drive on the road will increase by 0.307−0.480 conditional units; − if positive impression left by a road landscape increases by 1 conditional unit, the willingness to drive on the road will increase by 0.154−0.313 conditional units; − if pleasurability of a road landscape increases by 1 conditional unit, the willingness to drive on the road will increase by 0.147−0.315 conditional units; − if sophistication of a road landscape increases by 1 conditional unit, the willingness to drive on the road will increase by 0.079−0.260 conditional units; − if skittishness of a road landscape increases by 1 conditional unit, the willingness to drive on the road will increase by 0.076−0.218 conditional units. Fig. 2 illustrates unstandardized predicted values for the variable Would like to drive, their means and confidence intervals. Fig. 2a shows the mean values of unstandardized predicted values, lower mean 95% confidence intervals, upper mean 95% confidence intervals, lower mean individual 95% confidence intervals and upper mean individual 95% confidence intervals for the variable Would like to drive. Fig. 2b shows the unstandardized predicted values for lower mean 95% confidence intervals, upper mean 95% confidence intervals, lower mean individual 95% confidence intervals and upper mean individual 95% confidence intervals for the variable Would like to drive.
However, it is important to emphasize that other factors which were not included in the research have a greater impact on the willingness to drive on a certain road (|-0.515| > |0.393| > |0.234| > |0.231| > |0.169| > |0.147|). Moreover, the unknown variables reduce the willingness. The provided regression equation accounts for 60.2% of the total dispersion, while other factors determining the willingness to drive on a certain road remain trackless and reach 39.8% of the total dispersion. Thus, tracking the missing factors and forming a refined regression equation is a challenge for further research in the area.
The equation was also verified. The performed regression analysis rests on an assumption that intervals between values of all the variables are equal, therefore the ordinal variables were assimilated with interval ones, thus making implementation of regression analysis possible. However, additional measures, aimed at ordinal variables, help to indicate if the equation is valid. Table 1 shows rank (Kendall r tau_b ) and monotony (Spearman r s ) correlations of relevant variables with Would like to drive. All the variables are considered to be relevant, except for demographic characteristics which showed no or very weak correlation with Would like to drive (r tau_b = [|-0.003|; |0.134|], r s = [|-0.003|; |0.153|]).
Data in the Table 1 confirms not only the comparatively stronger correlations between the variable Would like to drive and other variables in the equation, but their sequence as well: Elements Match, Left Positive Impression, Pleasant, Sophisticated and, finally, Skittish. Thus, the equation is valid.

Guidelines for development of desirable to drive road landscape
It is also important to define how the descriptive variables are treated. The three best and the three worst evaluated pictures (means) of road landscape were distinguished by the independent variables in the equation. Based on them, some guidelines for the development of desirable to drive road landscape which satisfies aesthetic needs of consumers are provided ( Table 2).
As the Table 2 shows, the guidelines based on the analysis of the selected pictures are alike, though the pictures are different. The best visual examples of the respective aspects are shown in the Table 2. However, despite the different sceneries with varying trees, buildings, waters and supporting engineering infrastructures, the guidelines are universal to a large extent regardless of the certain scenery on a certain picture.  Further analysis of the data made it possible to select 25% of road landscape which was evaluated by respondents as the most Would like to drive (Fig. 3).
The mean evaluation measures of the pictures vary from 4.67 to 4.15, and the content is consistent with the guidelines derived from the analysis of the pictures by aspects.

Identification of Would like to drive road landscape
For the identification of Would like to drive road landscape (the case of the main Lithuanian roads), the method of descriptive statistics was applied. Table 3 demonstrates distribution of roads' landscape into five groups according to the respondents' opinion: from Would not like to drive to Would like to drive. The marked cells indicate the highest response percentages for each road.
Obviously, the landscape of all the roads is neutrally or positively evaluated. There is no road landscape which was distributed into Would not like to drive or Would better not drive groups. This phenomenon is explained by the cultural features of Lithuanian consumers: they are not keen to evaluate items very badly in order to avoid extremeness. A typical Lithuanian consumer evaluates an unlikely item as I don't know or I have no opinion on this case instead of I don't like it. Supposedly, a part of road landscape which was distributed into the group Neither would like nor would not like to drive has to be in the group Would better not drive or even Would not like to drive. The landscape of A6 road (Kaunas-Zarasai-Daugpilis) is identified as Would like to drive road landscape. It is a historic road and it was a very important post road of the 19 th century between St. Petersburg (Russia) and Warsaw (Poland). Still some buildings of post stations, pikes, passengers' houses and lodges of the 19 th century are located along the road. In Zarasai region the road is surrounded by lakes. This road landscape has a great aesthetic, cultural and economic potential. The case of A13 road landscape demonstrates the equal amount of percentage in both Neither would like nor would not like to drive and Would better drive groups. However, the fact that this road landscape has 9.0% in the group Would like to drive distributes A13 road landscape into the group Would better drive. fig. 3. The most Would like to drive road landscape (by respondents). Note: road number is shown in the lower left corner of each picture