Research Article: 2020 Vol: 24 Issue: 2
Amit Tariyal, IMS Unison University
Shalini Singh, IMS Unison University
Swati Bisht, IMS Unison University
Purpose: In this era of information, communication, and technology where the population of millennials has a high consumption of internet, the impact of information available on the internet plays a vital role in shaping the attitude of this segment. Millennial population loves to hang out and with this segment the most popular hangout places are cafes. Café visits are therefore on a high, especially with the urban millennial population. This paper is an attempt to understand the influence of electronically given reviews by customers on their decision making with respect to café visits in the Dehradun region of this segment. This paper tries to evaluate and assess the impact of varied factors considered by people while making a decision on which café to visit. Methodology: A structured questionnaire was emailed to 350 respondents of the Dehradun region. 300 respondents acknowledged the questionnaire and responded back. After the screening process, a total of 286 comprehensive questionnaires were admitted. An exploratory factor analysis was applied to identify the important factors considered by people while going through e-reviews on their café visits. Regression analysis was further conducted to evaluate the impact of those factors on cafe visits. Findings: Results show a significant impact of all the four identified factors that evolved through the exploratory factor analysis on the cafe visit. These four factors show the importance of 4Ps here i.e. Place (Ambience), Process (Order processing), Product (Food and Beverage), and People (Staff). Place (Ambience) is found to be the most important factor that customers consider while checking out the e-reviews about cafes followed by order, food & beverage, and staff. Limitations: This study has some limitations related to the geographical region, sample, research methodology and tools. Other factors influencing the cafe visit may also be included in the future.
E-reviews, Millennial, Cafe Culture & Visit, Decision Making, Dehradun.
India is a growing economy and the disposable income of the people is on the rise. India will soon be the youngest country in the world by 2020 and will have a large population of millennials. Dehradun, the capital of the Indian state of Uttarakhand is located near the foothills of the Himalayan region. The city is acclaimed for its attractive landscape and soothing climate and provides a gateway to the surrounding region. The region also headquarters various public and defence organizations. Besides this, the region also boasts of providing a high quality of education at different levels due to which there is a huge inflow of millennial population from in and around the world. Millennials are people born between the years 1981-1996. With this growing population of millennials, there will be opportunities for a large number of businesses to explore their business visions here. India will offer a demographic advantage that will make it a focus of the marketers of the world. The generation of Millennial is a highly informed generation compared to any other previous generation. This is courtesy of the power of the internet and the ability of this generation to easily navigate and make most of it. This is also a reason why this generation is also called Digital Natives. The Millennials are demanding, confident and high on their fun quotient and at the same time are high on achievement quotient. They have the power to get influenced by their peer group and at the same time have the power to influence others. They love to socialize and be a part of a social group. Millennials have been categorized as open-minded, social, innovative, energetic, ambitious, reliable, motivated, and intelligent young people (Ordun, 2015).
Their consumption habit for products and services is dynamic because they like experimenting. A lot of the time these digital natives are found in restaurants, cafes and other hangout places that reflect their personality in one way or another. Cafes represent hang out places for the millennial. Cafes are places where the youth socialize, express and have fun together with their friends and peer group. In short, it will not be wrong to call cafes as ‘My kind of a place’ for the youth. Cafes are typically present in a lot of urban and suburban areas. Most of the cafes have a creative concept and a relaxing ambience because of which cafes are usually more exclusive and premium on their pricing compared to a regular restaurant (Bernson, 2011). Cafes go better with the image of the youth perceptually and this has given rise to the so-called ‘Café Culture’. The cafe culture is getting popular and catching up with this generation in most cities across India and of late one can witness a meteoric rise in the number of cafes in the country. Cafes present an aspiration value for this generation and provide experience. It today not only represents a place to socialize and have a conversation but also provides a platform to colleagues to share ideas, brainstorm and even engage in business conversations.
Kim (2014) in a study found that the younger generation has an inclination towards conspicuous consumption. According to Yoong (2014), the young population patronize luxury restaurants and cafes and take it as a means for self-expression or symbols of their desired lifestyle. Their café visit depends upon different factors, especially feedback provided by the peer group. Now in this era of internet e-reviews is found to be comparatively influencing than the traditional reviews.
The study aims at understanding the influence of e-reviews on the choice of café or café visit by the millennial and ascertaining the impact of different variables of reviews on café selection.
Theoretical background
Factor considered by millennial while checking out the e-reviews of the cafe
The origin of consumer behaviour and understanding of their buyer behaviour goes back to the origin of mankind where needs existed and there were ways and means identified by individuals to fulfil them. There are many theories that support behavioural studies and purchase intentions and explain the contributing factors related to a given purchase decision. The existing research helps in the understanding of purchase behaviour and purchase decisions and factors that support a specific behaviour. There are changes taking place in the hospitality sector and because of this there is high pressure from competitors and thus increase in the expectations of the consumer. Such reasons have made it even more important for a hospitality service entrepreneur or business person to develop a better understanding of how to foster and maintain satisfaction and loyalty in customers (Sundaram et al., 1998).
Wróblewski (2016) in a study discovered that the socio-cultural environmental factors or marketing activities that a company undertakes, along with a few other things, impact purchase-related decisions. A few types of research conducted in the past reveal that the consumer’s online reviews act as an important element of the marketing mix and facilitates more sales (Chen & Xie, 2008).
The three main categories the youth intend to revisit a café that has e-atmospherics are cosy ambience, availability of Wifi and beautiful facility (Jalil et al., 2015). Jaw et al. (2010), in an interesting study, found that offering free Wi-Fi is a way to attract more customers. A good store location is a determining factor that decides if the store can pull a crowd (Hung, 2012). Poor or Unavailable Wi-fi facilities will be a discouraging factor for the revisit of the youth to the eating joint. Another research (Lieshout & Rodriquez, 2007) identifies that people may show a tendency to leave the cafe that doesn’t provide them with wi-fi.
Sathish & Venkatesh Kumar (2011) found that a good atmosphere has a positive influence on the time spent by the customer at the cafe and this also leads to an increase in their impulse purchases. Quality of a product is found to have a direct impact on the level of customer satisfaction which in turn has an influence on the decision to purchase a product or services (Wang et al., 2013). The quality of service delivered is important, but how the customer perceives the quality of the given service is of much greater relevance (Seltz, 1983). Cleanliness and hygiene as attributes is of great relevance (Mamalis, 2009)
Hui (2011) identified unfavourable café atmospheric factors like dirty toilets that lead to unpleasant odour), poor condition of the floor and untidy uniform/apron. Kelman (1958) identified that customers' purchase decisions can be affected by three different modes of social influences. They are identification, compliance and internalisation. Identification happens when a person is influenced by the social status of the group of believers and wants to be recognised and be associated with the group without question. This happens when this person holds the other person with a lot of admiration. Compliance happens when someone simply does what other people want him or her to do, by following up on a suggestion or a request. Normally during any compliance process, people tend to change their behaviour in public but keep their private beliefs intact. This does not need an investment of emotions. Internalisation is the strongest of all influences and is made on a long-term basis. In this, public behaviour and private beliefs are changed at the same time.
Brown & Reingen (1987) in their study discovered that strong social bonds like close friends and family members have considerably higher influential power in Word of Mouth WoM communication compared to weak social ties between strangers or acquaintances. In contrast to offline or the face to face WoM (where opinions on products are usually expressed verbally and can even be missed, electronic WoM persists on the Internet and has the power to get retrieved at any time (Dellarocas et al., 2007). It has been found in studies (Mangold & Smith, 2012) that with Millennials there is a likelihood of trusting opinions of people over those of companies. Millennials have a tendency to make use of the way they consume, and also what they consume in order to showcase to the world for who they are as individuals (Parment, 2013).
Hansen et al. (2005) identified the core product (which encompasses more than food and beverage), the cafe interior, the personal social meeting, the company and the cafe atmosphere as relevant to the decision making in cafes. Campbell-Smith (1967) identified that food and drink, service, ambience and atmosphere, value for money and cleanliness-hygiene are important factors kept in mind while selecting an eating joint.
Sparks & Browning (2011) explored the four key factors (target of the review; overall valence of a set of reviews; framing of reviews; and consumer rating) which impact the purchase intention. The results indicate that primary service quality dimensions leading to online customer satisfaction, with the exception of ease of use, are closely related to traditional services while key factors leading to dissatisfaction are tied to information systems quality (Yang et al., 2003).
The results of exploratory factor analysis revealed four factors or dimensions of customer experience, namely, tangible and sensorial experience, staff aspect, aesthetic perception, and location (Ren et al., 2016).
This study aims to compare the customer satisfaction index (CSI) based on two approaches: stated‐importance and derived importance approaches. The stated‐importance approach uses both importance and performance scores in constructing the CSI, while the derived‐importance approach uses regression analysis to derive the betas for calculating CSI. The results show that the stated importance approach has achieved a higher CSI (79.1 per cent) than that of the derived importance approach (57.4 per cent). Both approaches find that the aspects of rooms and employees are the most important factors in driving customer satisfaction. Strengths and weaknesses of the two approaches are discussed (the Chu, 2002)
The traditional use of customer surveys as a method of obtaining customer feedback takes on a new dimension when the importance-performance analysis technique (IPA) is used. Customer survey data are plotted on a two-dimensional grid to show the relationship of the importance of an attribute of the college food service operation to the customer as well as the performance of the foodservice on the attribute. This approach to customer survey analysis is particularly useful to administrators/managers in the development of marketing strategies for each campus foodservice operation to meet the needs of the college customer. (Green, 1992)
Previous studies also advocated the association between service recovery and customer satisfaction. Service quality maintained is the key factory to enhance customer satisfaction and word-of-mouth communication plays an important role for the same. (Murray, 1999). Factors related to a cafe like food presentation, taste, spatial seating arrangement, fascinating interior design, pleasing background music, reliable service, responsive service, and competent employees are considered as the important variables enhancing customer satisfaction and hence the customers’ visits (Namkung et al., 2008).
E-reviews
The word of mouth is the powerful tool used to convey the information about product and company to the users. (Dellarocas, 2003). The previous study also suggested that the online reviews entailing intrinsic product information influences the consumer buying behaviour and also provides the platform for comparative evaluation for the product or services (Duan et al., 2007; Gobinda et al. 2017).
It also indicates that the extent of WOM search depends on the consumer's reasons for selecting the product and service provider (Chatterjee, 2001). Due to the transition in retail and business settings word of mouth has also been extended to the electronic word of mouth because of an increase in electronic media and internet usages. (Yang, 2017). (Singh &Verma, 2018) advocated the impact of website communication including online reviews on buying behaviour. Kim (2014) in his study discovered that the young generation has an inclination towards noticeable consumption and patronize luxury cafes and cafes, which acts as a means to their self-expression or symbol of a lifestyle they desire.
Chen & Xie (2008) proved that online reviews given by users have become a new element in the marketing communication mix. Moe & Trusov (2011) in their study demonstrated that consumers purchase behaviour is affected significantly by the positive ratings from other users given previously. It has also been seen that Social media platforms have become a powerful tool for electronic word of mouth because of the ability to globally disseminate information and ubiquity (Choi & Kim, 2014; Eisingerich et al., 2015). Along with online reviews, the online rating given by the user consumers also impact the sales but to some extent (Öğüt et al., 2012). Öğüta & Cezar (2012) discussed the factors like room size, price etc. which motivate the customers to write reviews about the hotels with low ratings. These reviews are not as such value for the hotels having high star ratings. Zhang et al in 2010 evaluated the impact of electronic-word-of-mouth on the restaurant’s popularity and also did a comparative study between consumer’s reviews and editor’s reviews (Table 1).
H1: E-reviews have a significant impact on the café visits of millennial
| Table 1: Factors Considered by Millennial While Checking Out E-Reviews | ||
| Factor affecting café visit of millennial | Factors | Major studies |
|---|---|---|
| Food and Beverage | Food quality, variety, portion size | Wang et al. (2013); Campbell-Smith (1967); Namkung et al. (2008) |
| Marketing Factors | Promotion, Price, Value for money, Brand, Place | Wróblewski (2016); Parment (2013); Barreda et al. (2015); Jansen et al. (2009); Han et al. (2019); Ho-dac et al. (2013); Neirotti et al. (2016); Hashim & Fadhil (2017); Sparks et al. (2016); Ren et al. (2016); Green, (1992) |
| Staff Responsiveness and Attributes | Staff behaviour, services, query handling | Ren et al. (2016); Chu, (2002); Green, (1992); Murray et al. (1999); Namkung et al. (2008) |
| Service Quality | Reliability, empathy, responsiveness, assurance, tangibility | Sundaram et al. (1998); Seltz (1983); Campbell-Smith (1967); Green, (1992); Namkung et al. (2008) |
| Tangibility | Sense of touch, physical evidence | Jalil et al. (2015); Jaw et al. (2010), Hung (2012), Lieshout & Rodriquez (2007); Sathish & Venkatesh (2011); Mamalis (2009); Hui (2011); Campbell-Smith (1967); Ren et al. (2016); Green, (1992); Namkung et al. (2008) |
| Social Factors | Peer group, cultures, social norms, family | Brown & Reingen (1987); Wróblewski (2016); Mangold & Smith (2012); Parment (2013); Hansen et al. (2005); |
| Online Reviews and Communication | Website communication, customer’s reviews, editor’s reviews | (Chen & Xie, 2008), Dellarocas et al. (2007); Herr et al. (2008); Sparks & Browning, (2011); Dellarocas (2003); Duan et al. (2007); Gobinda et al. (2017); Chatterjee (2001); Yang (2017); Singh &Verma (2018); Kim (2014); Chen and Xie (2008); Moe & Trusov (2011); Choi & Kim (2014); Eisingerich et al. (2015); Öğüt et al. (2012); Öğüta & Cezar (2012); Hashim& Fadhil (2017). |
| Service Process | Order, billing, service delivery, service quality | Kelman (1958); Green, (1992); Namkung et al. (2008) |
Research Design
For this study, an online questionnaire has been administered on the basis of factors identified from the review of the literature to evaluate the impact of e-reviews on cafe visits decision making of millennial. Convenience sampling has been used to collect the data from millennials from Dehradun region via online Google form.
Descriptive Statistics of Respondents
A total of 350 questionnaire were sent through google form to the millennial respondents out of which 300 respondent acknowledge and responded back with filled questionnaire survey. After the data cleaning process, 286 responses were found to be effective. When the respondents were asked whether they check online reviews before visiting a new cafe 78 respondents out of 286 (24 females and 54 males) admitted that they do not check online reviews before visiting a new cafe while 208 respondents out of 286 (88 females and 120 males) admitted that they often check online reviews before visiting a new cafe. Under the marital status category, the major proportion of respondents were found unmarried i.e., 208 out of 286 who consider e-reviews before visiting a new cafe. Majority of employed and unemployed respondents show their agreement for checking online reviews. Across the different education groups as well as different income groups of respondents, most of the respondents accepted that they check e-reviews before visiting a new cafe. Results of descriptive statistics shown in Table 2 represent the large number of respondents who check the e-reviews before visiting a new cafe.
| Table 2: Descriptive Statistics of Respondents | |||
| Do you check online reviews before visiting a new cafe? | |||
|---|---|---|---|
| No | Yes | ||
| Count | Count | ||
| Gender | Female | 24 | 88 |
| Male | 54 | 120 | |
| Marital Status | Married | 8 | 46 |
| Unmarried | 70 | 162 | |
| Employment Status | Employed | 28 | 88 |
| Unemployed | 50 | 120 | |
| Personal Income Range | 2 lakhs - 4 lakhs | 14 | 28 |
| 4 lakhs - 6 lakhs | 0 | 24 | |
| Less than 2 lakhs | 6 | 14 | |
| More than 6 lakhs | 8 | 24 | |
| No Income | 50 | 118 | |
| Education Level (Highest Degree only) | Doctoral | 4 | 12 |
| High School | 2 | 6 | |
| Intermediate | 4 | 14 | |
| Post Graduate | 24 | 92 | |
| Undergraduate | 44 | 84 | |
The Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test are relevant sources to measure sampling adequacy. The rule of thumb says that if the KMO value is more than 0.7 it is suitable whereas it is considered insufficient if the value is less than 0.50. In addition, Bartlett's test is an inferential statistic used to evaluate the assumption that variances are equal in different samples. At the significance level of 5%, the correlation has been found among variables (Barrett & Morgan Jr, 2005). The KMO and Bartlett values (shown in Table 3) is 0.942 and 0.000 respectively which are within an acceptable range.
| Table 3: KMO and Bartlett's Test | ||
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy | 0.942 | |
| Bartlett's Test of Sphericity | Approx. Chi-Square | 4559.897 |
| df | 378 | |
| Sig. | 0.000 | |
A total of 286 responses have been gathered to measure scale reliability. All the 27 variables are identified significantly as the communality value is more than 0.5 (shown in Tables 4 & 5). A communality is an extent to which a variable correlates with all other variables. 9 variables are excluded from the analysis as their communality value was less than 0.5.
| Table 4: Communalities Values Communalities | ||
| Initial | Extraction | |
|---|---|---|
| [Food quality and presentation] | 1.000 | 0.823 |
| [Food taste] | 1.000 | 0.849 |
| [Beverage quality and presentation] | 1.000 | 0.844 |
| [Beverage taste] | 1.000 | 0.859 |
| [Portion size] | 1.000 | 0.756 |
| [Portion Consistency (e.g. - Every time you order for a dish, you get same quantity)] | 1.000 | 0.791 |
| [Value for money] | 1.000 | 0.746 |
| [Promotional offers] | 1.000 | 0.690 |
| [Reasonable prices of food and beverage items] | 1.000 | 0.650 |
| [Staff responsiveness] | 1.000 | 0.879 |
| [Staff attitude and behaviour] | 1.000 | 0.795 |
| [Service consistency (e.g. - Quick service provided by staff every time when you place an order)] | 1.000 | 0.844 |
| [Safety and security of guest and their belongings] | 1.000 | 0.759 |
| [Staff knowledge (e.g. - they provide you details of the dishes available in the menu)] | 1.000 | 0.793 |
| [Parking my vehicle] | 1.000 | 0.724 |
| [Finding/Booking a table] | 1.000 | 0.806 |
| [Placing an order] | 1.000 | 0.872 |
| [Getting the order] | 1.000 | 0.834 |
| [Billing] | 1.000 | 0.831 |
| [Scenic view] | 1.000 | 0.687 |
| [Good ambience] | 1.000 | 0.744 |
| [Neat and clean sitting area] | 1.000 | 0.797 |
| [Comfortable seating place] | 1.000 | 0.837 |
| [Hi-chairs for toddlers] | 1.000 | 0.688 |
| [Wheelchair facility for physically challenge] | 1.000 | 0.674 |
| [Soothing music] | 1.000 | 0.674 |
| [Neat and clean washroom] | 1.000 | 0.743 |
Extraction Method: Principal Component Analysis.
| Table 5: Total Variance Explained | ||||||||||
| Component | Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | ||
| 1 | 15.998 | 57.137 | 57.137 | 15.998 | 57.137 | 57.137 | 7.106 | 25.378 | 25.378 | |
| 2 | 3.731 | 13.325 | 70.462 | 3.731 | 13.325 | 70.462 | 6.047 | 21.598 | 46.976 | |
| 3 | 1.222 | 4.366 | 74.827 | 1.222 | 4.366 | 74.827 | 4.711 | 16.825 | 63.801 | |
| 4 | 0.897 | 3.204 | 78.031 | 0.897 | 3.204 | 78.031 | 3.984 | 14.230 | 78.031 | |
| 5 | 0.680 | 2.430 | 80.461 | |||||||
| 6 | 0.636 | 2.273 | 82.734 | |||||||
| 7 | 0.510 | 1.822 | 84.555 | |||||||
| 8 | 0.455 | 1.626 | 86.182 | |||||||
| 9 | 0.427 | 1.524 | 87.705 | |||||||
| 10 | 0.392 | 1.399 | 89.104 | |||||||
| 11 | 0.342 | 1.223 | 90.327 | |||||||
| 12 | 0.307 | 1.097 | 91.424 | |||||||
| 13 | 0.297 | 1.061 | 92.484 | |||||||
| 14 | 0.261 | 0.933 | 93.418 | |||||||
| 15 | 0.214 | 0.763 | 95.008 | |||||||
| 16 | 0.191 | 0.683 | 95.691 | |||||||
| 17 | 0.178 | 0.637 | 96.328 | |||||||
| 18 | 0.168 | 0.599 | 96.927 | |||||||
| 19 | 0.149 | 0.531 | 97.458 | |||||||
| 20 | 0.136 | 0.485 | 97.943 | |||||||
| 21 | 0.127 | 0.452 | 98.395 | |||||||
| 22 | 0.107 | 0.381 | 98.776 | |||||||
| 23 | 0.088 | 0.314 | 99.090 | |||||||
| 24 | 0.080 | 0.285 | 99.375 | |||||||
| 25 | 0.062 | 0.223 | 99.598 | |||||||
| 26 | 0.062 | 0.221 | 99.819 | |||||||
| 27 | 0.051 | 0.181 | 100.000 | |||||||
The variance explained by each component (factor) evolved from the factor analysis i.e. component 1,2,3,4 are 25.378, 21.598, 16.825 and 14.230 respectively. A total variance explained by the four components is equal to 78.031(78.3%) which means that café visit decision making is sufficiently explained by these four factors.
The result of the rotated component matrix shows the four factors are having 9, 8, 5 and 5 variables respectively shown in Table 6. The rotated component matrix or factor loading is the significant output using varimax rotation that encloses estimates of the correlations between each of the variables and the estimated components.
| Table 6: Rotated Component Matrix | ||||
| Component | ||||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| [Food quality and presentation] | 0.755 | |||
| [Food taste] | 0.787 | |||
| [Beverage quality and presentation] | 0.798 | |||
| [Beverage taste] | 0.793 | |||
| [Portion size] | 0.815 | |||
| [Portion Consistency (e.g. - Every time you order for a dish, you get same quantity)] | 0.793 | |||
| [Value for money] | 0.647 | |||
| [Promotional offers] | 0.598 | |||
| [Reasonable prices of food and beverage items] | 0.622 | |||
| [Staff responsiveness] | 0.660 | |||
| [Staff attitude and behaviour] | 0.623 | |||
| [Service consistency (e.g. - Quick service provided by staff every time when you place an order)] | 0.696 | |||
| [Safety and security of guest and their belongings] | 0.651 | |||
| [Staff knowledge (e.g. - they provide you details of the dishes available in the menu)] | 0.649 | |||
| [Parking my vehicle] | 0.714 | |||
| [Finding/Booking a table] | 0.778 | |||
| [Placing an order] | 0.775 | |||
| [Getting the order] | 0.715 | |||
| [Billing] | 0.679 | |||
| [Scenic view] | 0.768 | |||
| [Good ambience] | 0.811 | |||
| [Neat and clean sitting area] | 0.826 | |||
| [Comfortable seating place] | 0.857 | |||
| [Hi-chairs for toddlers] | 0.802 | |||
| [Wheelchair facility for physically challenge] | 0.783 | |||
| [Soothing music] | 0.781 | |||
| [Neat and clean washroom] | 0.779 | |||
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
The Table 7 shows the correlation among the four independent variables (including food and beverage, ambience, order and staff) and the dependent variable i.e. cafe visit.
| Table 7: Correlations | ||||||
| Cafe visit | F & B | Ambience | Order | Staff | ||
|---|---|---|---|---|---|---|
| Pearson Correlation | Cafe visit | 1.000 | 0.193 | 0.764 | 0.227 | 0.183 |
| F & B | 0.193 | 1.000 | 0.000 | 0.000 | 0.000 | |
| Ambience | 0.764 | 0.000 | 1.000 | 0.000 | 0.000 | |
| Order | 0.227 | 0.000 | 0.000 | 1.000 | 0.000 | |
| Staff | 0.183 | 0.000 | 0.000 | 0.000 | 1.000 | |
| Sig. (1-tailed) | Cafe visit | 0.000 | 0.011 | 0.000 | 0.003 | 0.014 |
| F & B | 0.011 | 0.000 | 0.500 | 0.500 | 0.500 | |
| Ambience | 0.000 | 0.500 | 0.000 | 0.500 | 0.500 | |
| Order | 0.003 | 0.500 | 0.500 | 0.000 | 0.500 | |
| Staff | 0.014 | 0.500 | 0.500 | 0.500 | 0.000 | |
The model summary chart shown in Table 8 predicts the R square value i.e. 0.706 which shows the strong impact of the independent variable on the dependent variable. It means that the regression model shows good robustness.
Table 8: Model Summary of the Regression Model |
||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
|---|---|---|---|---|
| 1 | 0.840 | 0.706 | 0.697 | 0.46254 |
a. Predictors: (Constant), Staff, Order, Ambience, F & B
b. Dependent Variable: Cafe visit
The ANOVA Table 9 shows significant results and acceptable F statistics, that reflects the variability among the independent variables i.e. F & B, staff, order and ambience.
| Table 9: Anova Result | ||||||
| Model | Sum of Squares | Df | Mean Square | F | Sig. | |
|---|---|---|---|---|---|---|
| 1 | Regression | 70.756 | 4 | 17.689 | 82.682 | 0.000a |
| Residual | 29.524 | 138 | 0.214 | |||
| Total | 100.280 | 142 | ||||
a. Predictors: (Constant), Staff, Order, Ambience, Fand B
b. Dependent Variable: Cafe visit
The regression equation evolved from the analysis is given below:
| Table 10: Coefficient result | |||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Cronbach alpha | ||
|---|---|---|---|---|---|---|---|
| B | Std. Error | Beta | |||||
| 1 | (Constant) | 4.601 | 0.039 | 118.963 | 0.000 | 4.601 | |
| F & B | 0.162 | 0.039 | 0.193 | 4.168 | 0.000 | 0.162 | |
| Ambience | 0.642 | 0.039 | 0.764 | 16.542 | 0.000 | 0.642 | |
| Order | 0.190 | 0.039 | 0.227 | 4.906 | 0.000 | 0.190 | |
| Staff | 0.154 | 0.039 | 0.183 | 3.956 | 0.000 | 0.154 | |
a. Dependent Variable: Cafe visit
Cafe visit = 4.601 + 0.162 (Food & Beverage) + 0.642 (Ambience) + 0.190 (Order) + 0.154 (Staff)
The above regression equation shows the value of alpha value is 4.601 and the regression weights of the four predictors are 0.162, 0.642, 0.190 and 0.154. The equation clearly reflects the ambience predictor as the most important factor influencing cafe visit followed by order process, food & beverage (F & B) and staff. A total of four factors have been extracted from the exploratory factor analysis including 19 variables. The Cronbach alpha values for these four factors are found to be in the acceptable range i.e. more than 0.7 (Tavacol & Dennick, 2011; Devellis, 2016).
Implications
This paper reveals the practical and significant insights for the cafe business which can be utilized by the cafe owners and management for optimizing the e-reviews in a competitive way. Previous research shows the imperative role of e-reviews on the cafe visitors and hence strategists are using e-reviews to create an everlasting impact on the visitor’s mind. This study evolved four significant predictors of e-reviews impacting the cafe visits i.e. food & beverage, ambience, order process and staff. It is clear from the result that the millennial gives utmost priority to ambience followed by other predictors. Hence the managerial experts can utilize the positive e-reviews about ambience to attract more visitors. The result also shows the significant impact of e-reviews about order process, food & beverage and staff in influencing the cafe visit of millennials.
In the era of internet where most of the millennial are frequent users of the internet and often indulged in the e-reviews, it should be the mandatory and prerequisite practice for the cafe management to cultivate the profit from e-reviews by attracting the millennial.
Limitation and future scope of the study
Every research has some limitations and thus always leads to the scope of future studies. This study also has some limitations related to geographical region, sample, methodology and tools. The more diversified sample in terms of different demographic and social variables may be considered for drawing more valuable insights. The study was done on millennials; therefore, the generalization of the finding and implications may be checked or verified on other population segments. Future research may include the wider geographical areas and also the impact of e-reviews on other types of restaurants and hotels can be taken into consideration. In future different predictors i.e. independent variables may be included in this regression model if required as per the dynamic changes in lifestyle, technology and consumer behaviour. The moderation and mediation effects of different factors may also be considered in future to evaluate the validity and applicability of this regression model in different contexts.
This study precisely concludes the imperative impact of different factors that millennial considers while checking out the e-reviews on the cafe visits. The four factors which significantly impact the cafe visit are variables associated with food & beverages, ambience, order and staff which primarily include the seven Ps i.e., product, price, place, promotion, people, process and physical evidence. This paper highlights the significance of e-reviews on millennial perception and decision-making. Managers and cafe administration may get benefits by focusing on the four factors discussed in this paper so that positive e-reviews about the cafe ambience, food & beverages, order and staff can be shared by the visitors that will finally lead to more cafe visits.