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ROOT OF SMARTPHONE; ROUTE FOR A SMART BUSINESS @ RESTAURANT

Dr. S. Ganapathy#1   Reguraman Mugeshkannan#2  

#1 Professor, Department of International Business and Commerce, Alagappa University, Karaikudi, Sivagangai-dist., Tamilnadu, 09442677764, ganapathysuruthi@gmail.com

#2 Research Scholar, Department of International Business and Commerce, Alagappa  University, Karaikudi, Sivagangai-dist., Tamilnadu,  09791595635, mugesh.kannan1910@gmail.com

 

ABSTRACT

Successful services organizations understood the importance of evaluating and managing customer satisfaction. Have no existing technology with more competently and more universally usurped the landscape than mobile technology. Smartphone are used for social interaction, financial transaction, employee productivity and academic activity. With such significant usage, Smartphone have established omnipresence on restaurant experience, where the Consumers are using them for all aspects of their restaurant experience. The purpose of this paper is to analyze the use of Smartphone and Smartphone apps as a tool to promote customer experience on restaurant, especially when reading information about nutrition, ordering food, drinks and payment process and enjoyment in restaurant. This paper also aims to develop a Conceptual model to test users’ intention to use Smartphone apps as tool to promote customer experience on restaurant.  This study employs a survey instrument to assess restaurant business initiative with use of Smartphone for experience, survey made from the Tier II & Tier III cities of Tamilnadu and Compare this behavior to traditional format when administered at ahead of Commence.

Keywords: Smartphone, customer readiness, existing experience, technology acceptance and consumer behavior

ROOT OF SMARTPHONE; ROUTE FOR A SMART BUSINESS @ RESTAURANT

Introduction

Smartphone (including tablets) are becoming far more influential than the desktops. It has revolutionized many sectors such as Education, Telecom, Food, Medical etc.  Dual Core 2-GHz processors are shipping before now and Multi Core 4-GHz processors are on the horizon. The emergence marvelous growth of the new technology has unleashed powerful forces that are reshaping the business. Given the dynamics at work with the new technology, businesses can maintain operational excellence and implementing essential changes. The industry will survive and prosper depends on their ability to align themselves with new strategy implementation. Industry’s strategies should be congruent and complementary with its structure. The internal structure can be a cause of strength that provides competitive advantage for the industry. The suitable strategy and structure is expected to possess a positive outcome upon the industry. This study concerned with the Restaurant business and analyzing the Restaurant Omni services accomplish by the Smartphone. In these days foods are deducting human life stage, contamination of inorganic and other country foods are decimating the original value of existing foods. In that situation, people are analyzing the food nutrition details and they are trying to avoid health hazards and other displeasure in Restaurant while having food. Waiter, tip and payment gateway and enjoyment, these services are important displeasure in Restaurant experience. This study is to rectify or change the services those which are creating displeasure to consumer in Restaurant.

Process

Hi speed network connected inside of the Restaurant - Restaurant application developed and maintained with nutritional information, food consumption behavior and regular dish update (with price accuracy) - Every table having the tablet (attached), tablet IP is configured with table number - Session starts - Order input goes to storage and digital TV read the input from database and displayed - Waiter service for the particular order after prepared – 3rd,4th process continuously processed until the session going to end – Order items with price listed into the Cart and bill will be shown on Smartphone display when the session ends by customer - Payment process done with help of various payment modes (especially restaurant’s apps wallet and smartcard) - Database checks the payment completion after the session ends - If the payment is made successfully thank you message will displayed - If not completed, the tablet sounding the alarm - Customer service team takes care and the payment made successfully, finally greeting message displayed for completes the experience on Restaurant.

Literature review

Smartphone Apps are acting as a new business communication media compared with customary physical store transactions.   Mobile channels are providing a very large quantity of unique value to customers, such as no temporal and spatial limitation, accuracy with express and availability of information (Rachuri,K.K., & Mascolo,C., 2011). In restaurant, food servers are frequently perform in a unique position to influence what customers choose, it may affect their enjoyment of food and evaluated the manipulation of positive and negative comments made by servers on food choice and acceptance (Edward J.S.A et al., 2005), results showed that only negative statements made by servers actually influence food choice but in all conditions, once a customer had chosen of dish, acceptability of that dish was not affected. Poor diets, inactivity and consequent obesity have become a global trend since 1980s where high levels of overweight and obese citizens are currently originate in most regions of the world (Malik et al., 2013). In particular, the USA experienced a dramatic rise in obesity for the last two decades. Among those citizens, more than one-third of adults and 16 percent of the child population are categorized as being obese and at the risk of many obesity related diseases due to the over consumption of dish calories and total grams of fat (Obesity in America, 2011). Many adults are not educated on how to eat a healthy dish and maintain diet from a young age. Therefore, they are still in need of help

to turn their life in the region through healthy eating and learning the benefits of yoga and exercise (Thomas and Mills, 2006). Numerous studies highlight the alarming eating behaviors of youth and the growing obesity rates of this cohort. However, there are limited evidences to enlighten the effective intrusion for preventing weight gain in this group (Hebden et al., 2012). (Jungsun (sunny) kim et al., 2012) established that extrinsic motivation in using Self Service Technologies directly influenced the likelihood of using kiosks and existing experience indirectly influenced the likelihood of using kiosks through customer readiness in both female and male groups and also exposed that both female and male respondents who recognized their roles in using Self Service Technology more clearly and more likely to use kiosks at Quick Service Restaurants. (Kincaid and Baloglu, 2005) discovered that customers’ previous experience with Self Service Technologies affects the main variables of customers (role clarity, ability and extrinsic motivation) at Restaurant. Numerous studies suggest the significant intervention methods for this cohort, which include easy access to offering support for planning, treatment and self-monitoring behavior (Larose et al., 2011; Strong et al., 2008). For instance, through previous experience with Self Service Technologies, a user will recognize extrinsic and intrinsic benefits generated from the production through a Self Service Technology (e.g. fast transaction, feeling independent). It is reasonable to assume that potential users without experience of using Self Service Technologies will be less motivated by the potential benefits from Self Service Technologies. Furthermore, experienced Self Service Technology users are expected to have a better understanding of their roles given that they need fewer directions to understand their roles in the production process, compared to users without experience (Meuter et al., 2005). In particular, Kincaid and Baloglu (2005) discovered that the customers’ previous experience with Self Service Technologies affects the customers’ role clarity, ability and extrinsic motivation at restaurants. Overall, previous studies support that customers’ willingness (i.e. ability, role clarity, intrinsic motivation and extrinsic motivation) is affected by their previous experience with using Self Service Technologies at restaurant. Smartphone apps are innovative channels for delivering individual health behavior changes. They present a range of services that can promote the daily habits of their users. Smartphone apps allow users to keep up with their diets, exercise routines and overall health (Bendegul Okumus et al., 2014). Therefore, Smartphone Apps are present the opportunities for their users to establishing healthier lifestyle habits. For this regiment, it is important to offer support with self-monitoring of their actions. Smartphones are popular in this group and Smartphone Apps can develop the delivery of health

eating behavioral changes with regular update and easy payment with seamless experience to individuals in Restaurant.

Purpose of the study

To discover an appropriate target market, restaurant operators should understand possible influence of demographical factor on their customers’ acceptance level of Smartphones. Generally, young, single, highly educated and high income consumers are more likely to use mobile Applications. The implication is that it would be necessary for operator to continue examining Smartphone acceptance behavior across consumer to understand their usage behavior, information gathering, remittance through smart way and motivations.

(i). To understand the impact of customer experience with Applications on this likelihood of using Smartphone at Restaurant.

(ii). To understand a mediating role of customer readiness (Regular update, Expedition, Remittance, Stimulation)

Hypotheses of the study

H1. Having experience with Apps will increase Consumers’ likelihood of using Smartphone at restaurant.

H2. Regular update in Apps will lead to high levels of likelihood of using Smartphone at restaurant.

H3. Expedition of experience in using Apps will lead to high levels of likelihood of using Smartphone at restaurant.

H4. Remittance in using Apps will lead to high levels of likelihood of using Smartphone at restaurant.

H5. Stimulation in using Apps will lead to high levels of likelihood of using Smartphone at restaurant.

H6. There is no significant relationship between the Consumers’ previous experience with Apps, readiness for self service technology and likelihood of using Smartphone at restaurant based on gender.

 

Methodology

The proposed research model for this paper is shown in figure 1 and 2. In order to explore the variables in the proposed research model, a structured questionnaire survey was conducted. The sample was randomly selected from the restaurant’s panel members and is chosen from tier II &III cities. Out of 1000 questionnaire 700 respondents agreed to answer the questions. In total, 614 respondents completed answering all the questions.

 

Analysis

 

Table -1 Demographic characteristic of respondents

 

 

           n

   percentage

Gender

 

 

              Male

332

54.1

              Female

282

45.9

Age

 

 

              18-28 years

229

48.7

              29-38 years

166

27.0

              39-48 years

116

18.9

              Above 49 years

33

5.4

Education

 

 

              School level

5

0.8

              Under graduate

121

19.7

              Post graduate

225

36.7

              Professional course

263

42.8

Total

614

100

 

Table - 2 Indicators of the measurement model

Indicators of Regular update

RU1: update of dish and special dish menu day-by-day

RU2: personalization of the application to individual needs

RU3: the Apps shows the nutritional information menu items before ordering dish

Indicators of Expedition

EX1: speed in ordering dish or food and that reached at a time of touch

EX2: speed of delivering ordered food

EX3: Speed of Payment with technology

Indicators of Remittance

RM1: avoiding malpractice in payment and waiter’s tips

RM2: opportunities for payment with accurate amount by online payment modes

RM3: when I conduct transactions of amount in restaurant, a personal feeling of independence and worthwhile accomplishment is desirable

Indicators of Stimulation

ST1: convenience while ordering service/making transaction is desirable

ST2: when I conduct transaction in a restaurant, being fast is desirable

ST3: Personal feeling of enjoyment is desirable

 

Demographic characteristics of the respondents, 54.1 percent were male and 45.9 percent were female. Most of the respondents (48.7) were over 44 years old. 42.8 percent of the respondents, have completed their professional course, 36.7 percent of respondents have completed Post Graduate and another 19.7 percent of the respondents were completed their UG degree. To compare the mean score of the likelihood of using Smartphone and the readiness variables by gender group and the independent t-test was used for analysis of main variables (see Table 2: description of main variables & Table 3: mean comparison by gender). The results indicate the male respondents were more likely to use Smartphone at restaurant than female (p<0.001).

Each customer readiness factor (i.e. Regular update, expedition, Remittance and stimulation) consisted of four indicators. One indicators in the Regular update showed a higher mean score male than female. Specifically, male respondents were more likely to agree that update of dish and special dish menu day-by-day than female. However, there was no significant difference for the other two indicators of the regular update factor, and also there was no significant difference between the three indicators of the expedition factor across gender group. On the other hand, two indicators of the Remittance factor rotated out to be significantly different between genders. The male respondents more strongly believed payment opportunities through online (especially restaurant’s wallet and smartcard) and avoiding malpractice, waiter’s tips when they were in a restaurant. Lastly, the mean scores of all three indicators related to stimulation turned out to be significantly higher for the male group than the female group. In other words, the male respondents more strongly believed their personal feelings of convenience, fast and enjoyment as desirable at restaurant.

Measurement model

Measurement model specified four factors: Regular update (RU), Expedition (EX), Remittance (RM) and Stimulation (ST).  The degree of agreement on the statements for Regular update, Expedition, Remittance and Stimulation are used as theme indicators (Table 2: description of main variables). Every indicator was constrained for load only on the factors. There is no equality constraints on the factors loading were imposed and the factor co-variances to be estimated. (Fit indices are as follows: standardized RMR< 0.10; non-normalized and comparative fit index>0.90; Root Mean Square Error of Approximation (RMSEA) <0.10; x2: p>0.05 (when the sample size is large, it’s normal to have p<0.05). Among those indices the measurement model fits the data very well:  2 (n=614) = 130.529, p< 0.001, CFI = 0.986 standardized RMR = 0.027 and RMSEA = 0.053. In reliability test, Cronbach’s coefficient α-values of four factor all surpassed 0.7, indicating good internal consistency.                         

 

Table - 3 Mean Comparison by Gender

Mean ± SD

 

Total (n=614)

Male (n=332)

Female (n=282)

t-value

p-value

RU1 a

3.70 ± 0.81

3.77 ± 0.80*

3.61 ± 0.82*

2.516

0.012

RU2 a

4.16 ± 0.82

4.15 ± 0.85

4.18 ± 0.78

-0.557

0.578

RU3 a

3.63 ± 0.91

3.69 ± 0.94

3.57 ± 0.88

1.655

0.098

EX1 a

3.68 ± 0.92

3.74 ± 0.93

3.61 ± 0.92

1.799

0.073

EX2 a

4.08 ± 0.87

4.06 ± 0.91

4.10 ± 0.82

-0.503

0.615

EX3 a

4.19 ± 0.79

4.17 ± 0.85

4.21 ± 0.72

-0.491

0.624

RM1 a

4.04 ± 0.83

4.13 ± 0.82**

3.93 ± 0.82**

2.974

0.003

RM2 a

4.09 ± 0.81

4.20 ± 0.81***

3.96 ± 0.79***

3.718

0.000

RM3 a

4.19 ± 0.76

4.23 ± 0.78

4.14 ± 0.73

1.539

0.124

ST1 a

3.75 ± 0.91

3.85 ± 0.94**

3.62 ± 0.85**

3.173

0.002

ST2  a

3.52 ± 0.95

3.62 ± 0.95**

3.40 ± 0.95**

2.908

0.004

ST3 a

3.60 ± 0.87

3.68 ± 0.90**

3.50 ± 0.82**

2.578

0.010

SR b

3.67 ± 1.12

3.83 ± 1.07***

3.48 ± 1.14***

3.812

0.000

Note: Significant at: *p<0.05, **p<0.01 and ***p<0.001; a from the responses on a five-point Likert scale: from 1- Strongly disagree and 5- Strongly agree; RU-Regular update , EX-Expedition, RM- Remittance and ST- Stimulation; b from the responses on a five-point Likert scale: from 1- Very unlikely and 5- Very likely; SR-Likelihood of using Smartphone at Restaurant

 

Structural model

In order to evaluate the adequacy fit of the proposed model (figure 1 & 2) to data, a combination of fit indices was investigated. A good fit of the structural model yield:  2(n=614) =396.609, standardized RMR = 0.202, CFI = 0.947, p< 0.001, and RMSEA = 0.089.  The estimation of standardized parameter from the path analysis is shown in figure-3 (Full sample). The Wald test was used to investigate for redundant structural paths, but the result showed that path is not dropping in the model. Conversely, the result of the Lagrange Multiplier (LM) test recommended adding two significant parameters in the model (i.e. Expedition and Regular update & Remittance and Stimulation).

As shown in table - 5, the relationship between experience and customer readiness (i.e. regular update, expedition, remittance and stimulation) was investigated. The experience variable had a significantly positive effect on the expedition factor (β=0.374) and the stimulation factor (β=0.342) (p<0.05), but no positive effect on regular update and remittance. The experience variables also have no significant influence on the likelihood of using Smartphone at restaurants. The results imply customers who have experience with technology will be more likely to have higher expedition and have remittance in using apps compared to those who are don’t have a

previous experience. While, the standardized indirect effects are revealed that the experience variable (with full sample) to have a positive effect on the likelihood of using Smartphone at restaurants (β=0.155) through the main variables (p<0.5). This result indicates customers who have both experience and higher levels customer readiness in using apps will be more inclined to use Smartphone at restaurants.

Table-4 Factor loading in the measurement model

 

Factor 1

RU

(Regular update )

Factor 2

EX

(expedition)

Factor 2

RM

(Remittance)

Factor 4

ST

(stimulation)

RU1

0.750

 

 

 

RU2

0.926

 

 

 

RU3

0.904

 

 

 

EX1

 

0.908

 

 

EX2

 

0.934

 

 

EX3

 

0.895

 

 

RM1

 

 

0.847

 

RM2

 

 

0.878

 

RM3

 

 

0.851

 

ST1

 

 

 

0.861

ST2

 

 

 

0.866

ST3

 

 

 

0.841

Reliability

(Cronbach’s α)

0.89

0.94

0.89

0.89

 

The additional paths results showed that the expedition factor positively affects the regular update factor (β=0.689), and the remittance factor positively affects the stimulation factor (β=0.683). The influence of main variables to apps on the likelihood of using Smartphone, the regular update factor (β=0.116) and the expedition factor (β=0.373) are the important determinant factors of the likelihood of using Smartphone at restaurant (p<0.05), but the expedition and stimulation had insignificant effects. The results indicate consumers with higher levels of regular update and remittance in using apps will be more likely to use Smartphone at restaurant. The four factors of consumer readiness accounted for (Direct effect: 0.215)21.5 percent of the variance (R2) in likelihood of using Smartphone at restaurants (see Table VI for direct, indirect and total effects for standardized scores).

Table – 5 Direct, Indirect and total effects for standardized scores: with full sample

                                                                          Standardized (β)

 

Direct effect

Indirect effect

Total effect

Experience and Likelihood of using Smartphone

0.039

0.155*

0.194*

Experience and Regular update  (F1)

0.033

0.258*

0.290*

Experience and Expedition (F2)

0.374*

 

0.374

Experience and Remittance (F3)

0.342*

 

0.342

Experience and stimulation (F4)

-0.003

0.234*

0.230*

Regular update  (F1) and Likelihood of using Smartphone

0.116*

 

0.116*

Expedition (F2) and Regular update (F1)

0.689*

 

 

Expedition (F2) and Likelihood of using Smartphone

-0.072

0.080*

0.008

Remittance (F3) and Likelihood of using Smartphone

0.373*

0.061

0.434*

Remittance (F3) and Stimulation (F4)

0.683*

 

 

Stimulation (F4) and Likelihood of using Smartphone

0.090

 

0.090

R2

0.215

 

 

 

Note: Significant at: *p<0.05; n=614

 

 

 

 

The results of table - 6 indicated that gender group (male and female) those who are having higher remittance in using apps will have higher likelihood of using Smartphone. However, other customer readiness factors-regular update, expedition and stimulation- had no significantly direct effect on likelihood of using apps in both groups (table - 6 and figure - 3), and also have a significantly positive relationships between the expedition and the regular update factors (in the male group: β=0.733 and female group: β=0.632), as well as between the remittance and stimulation factors (in the male group: β=0.645 and female group: β=0.729). The result of the moderating effect of main variables between existing experience with apps and intention to use Smartphone at restaurants revealed that the experience have a positive indirect effect on the likelihood of using Smartphone at restaurants (in the male group: β=0.162 and female group: β=0.144) through the main variables in gender groups (p<0.5), This result implies that customers are have both experience and higher levels of enthusiasm in using apps will be more oblique to use Smartphone at restaurants. In summary, in the male’s model 21.7 percent (0.217)of the

variance (R2) in the likelihood of using Smartphone was explained by existing experience, regular update, expedition, remittance and stimulation, and in the female’s model, 17.8 percent (0.178) of the variance (R2) in the likelihood of using Smartphone was explained by these factors (table 6).

Table – 6 Direct, Indirect and total effects for standardized scores: male vs. female

                                                                          Standardized (β)

 

Male (n=332)

Female (n=282)

 

Direct effect

Indirect effect

Total effect

Direct effect

Indirect effect

Total effect

Experience and Likelihood of using Smartphone

0.047

0.162*

0.209*

0.043

0.144*

0.187*

Experience and Regular update  (F1)

0.033

0.266*

0.299*

0.032

0.242*

0.274*

Experience and Expedition (F2)

0.363*

 

0.363

0.384*

 

0.384

Experience and Remittance (F3)

0.343*

 

0.343

0.341*

 

0.341

Experience and stimulation (F4)

-0.008

0.222*

0.214*

-0.009

0.248*

0.239*

Regular update  (F1) and Likelihood of using Smartphone

0.102

 

0.102

0.096

 

0.096

Expedition (F2) and Regular update (F1)

0.733*

 

 

0.632*

 

 

Expedition (F2) and Likelihood of using Smartphone

-0.042

0.075

0.032

-0.036

0.060

0.024

Remittance (F3) and Likelihood of using Smartphone

0.367*

0.062

0.429*

0.334*

0.056

0.390*

Remittance (F3) and Stimulation (F4)

0.645*

 

 

0.729*

 

 

Stimulation (F4) and Likelihood of using Smartphone

0.097

 

0.097

0.077

 

0.077

R2

 

0.217

 

 

0.178

 

 

Note: Significant at: *p<0.05

 

 

 

 

 

 

 

Conclusion

This research paper has tried to examine the use of Smartphone apps as tool to promote healthy eating behaviors, avoiding intermediates, especially when ordering food in restaurant. Further, it aimed to develop a Conceptual model to Smartphone users’ intention to use Smartphone apps to promote smart payment from restaurant app’s wallet and smart card. It could avoid nuisance and irritation in standing the queue and amount accuracy trouble at restaurant payment counter. Based on the discussion throughout this paper it can be concluded that Smartphone apps can offer opportunities in making behavioral changes in eating nutritional foods, eliminating intermediates (order taker), and efficiency of bill making (cart), quick remittance from restaurant app’s wallet and motivate the younger with seamless experience and adoption of technology. Restaurant business operators are advised to consider developing such architecture model (figure 1) for reshaping your business with innovative technology.

 

 

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