Digital Strategies and Financial Success in Tourism Enterprises

Amit Mishra

, Shashi Kant Gupta

, Susheel Kumar Singh

and Prabhdeep Singh

University of Lucknow, UP, India

Eudoxia Research University, New Castle, U.S.A.

Heera Lal Yadav Balika Degree College Lucknow, UP, India

Shri Ramswaroop Memorial University, Lucknow, India

Keywords: Trust; Travel Agency; Electronic Commerce.

Abstract: This research aims to investigate how wholesalers build confidence in B2B trade in the travel sector. In

Taiwan, 868 travel agencies were surveyed, and 211 of the investigations had valid responses. The findings

indicate that there is a significant gap between industry trust and control trust, demonstrating merchants are

more focused on image & reputation management than on taking practical steps to increase control trust. The

research concluded by offering suggestions for how wholesalers may strengthen their trust-building practises

maintaining the retailers' trust attitudes and beliefs in future business dealings.

1 INTRODUCTION

It is commonly acknowledged that information and

communication technologies are developing quickly

and have changed how companies operate and how

they operate. The Tourism sector is a consumer of a

varied variety of information and a primary user of

these technologies. The method companies operate in

the tourism industry has been changed by technology,

particularly the way firms distribute their products to

consumers. Consumers need flexible, customized,

accessible, engaging products and interactions with

tourism organisations as more people book and seek

for all of their travel needs online. Thus, creative

practises and increased competitiveness are

increasingly needed by both tourist destinations and

businesses (Vieira et al. 2020). Consumers have

started looking for a more in-depth travel experience

as a result of the growth of the tourist sector and the

upgrading of the Chinese people's consumer concept,

and the standard of travel has progressively increased.

It gives greater weight to the customization and

distinctiveness of tourism and no longer prioritises

mainstream travel as the top travel choice. A key force

in the tourist business, personalised tourism is in line

with market growth trends and is growing in scope

daily. Many experts and investors are searching for

new tourism models. The tourist e-commerce platform

has a significant place in the tourism business & is

what is causing customised tourism e-commerce to

develop so quickly (Zhang, et al. 2020). The tourist

industry made up the majority of Portugal's exports in

2019 (19.7% of total exports) and contributed 8.7%.

The increased usage of the internet and e-commerce

platforms has led to substantial development in this

industry. Almost 192,000 adults over the age of 15 in

Portugal claim to have bought at least one travel-

related item online. Higher social class members,

middle/senior managers, and those between the ages

of 25 and 34 are more likely to engage in internet

buying. The same survey found that during the last six

years, there has been a 34% increase in the number of

internet tourists purchasing goods and services (Xie,

et al. 2022). The financial decision, planning, and

auditing may be done well with the help of reliable

financial performance analysis. The company's

financial performance may a comprehensive picture of

its success. There have been several kinds of research

on performance assessment in the tourist industry, but

the majority of them focus on assessing hotel

performances (Yin et al. 2022). Travelers may

increasingly do their full product research and

booking for vacations online. To achieve that, they

need a product that is adaptable, customizable,

accessible, and interactive, and that they can

communicate with travel agents about. E-commerce

may also help travel businesses shift their primary

income stream away from commission fees and

toward service fee collection (Mangiaracina, et al.

2020).

Mishra, A., Gupta, S., Singh, S. and Singh, P.

Digital Strategies and Financial Success in Tour ism Enterprises.

DOI: 10.5220/0012912000003882

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd Pamir Transboundary Conference for Sustainable Societies (PAMIR-2 2023), pages 665-668

ISBN: 978-989-758-723-8

665

2 LITERATURE REVIEW

Al-Wattar, et al., (2019) examined how accounting

information system integration and sustainability

reporting might improve financial performance in the

hotel sector. The results show that there is an

improvement in overall financial performance as a

consequence of the interaction between both the

accounting information system and the hotel

sustainability reporting. Sunayana, et al. 2019

evaluated how the use of e-commerce apps affected

the productivity of Indian travel businesses. The

findings showed a meaningful connection between the

two factors. Tang et al. 2021 investigated the

inadequacies of tourist e-commerce in terms of the

consumer experience, conducted customer e-

commerce satisfaction surveys, and drew conclusions

about the discontent of customers with tourism e-

commerce. According to the research findings, the

total customer satisfaction rate is 2.61238. The scale

vector's division shows that the majority of travel e-

commerce clients are typically satisfied. Zhu et al.

2019 evaluated the purchase prediction issue in the

context of e-tourism, a developing and dominant e-

commerce application. While broad ranges of research

on purchase prediction have been conducted, little

study has been done on the buying habits for tourism-

related goods. The happiness that Romanian

consumers experience after making e-commerce

purchases of travel services was studied by Raluca-

Florentina, T. 2022; this satisfaction may serve as yet

another incentive for experts to use blockchain

technology.

3 RESEARCH METHODOLOGY

Analysis of pilot-test: The survey instrument's

content validity was confirmed after further

consultation with two managers at the mid-level on a

few topics and modifications to account for field

practices. Class B & C travel agencies are often seen

as merchants, whereas aggregated travel agencies are

typically seen as wholesalers. A Likert seven-point

scale was used to create all of the questions. A total of

31 valid answers were received for the research via the

survey instrument's pilot test, which was sent to 40

Taiwanese travel agencies. This indicates a 78.5%

effective response rate. The results of the survey

showed that all constructs had Cronbach's values

between 0.87074 and 0.867681, and that all variables

were greater than 0.7, indicating a pretty good level of

reliability. Some items that decreased construct

validity were removed to guarantee the validity of the

survey instrument. The resulting survey instrument

consists of 19 items in total, including 11 questions on

trusting beliefs, 5 questions about trusting attitudes,

and 3 questions about transaction intents.

Variables measurement: The survey instrument was

developed using comments from numerous research.

The survey is comprised of 19 items. Furthermore, 2

managers in a senior travel agency had previously

been engaged once again about the suitability and

relevance of the survey questionnaire. Before sending

out the survey instrument, the clarity of the phrasing,

topic, and field terminology was modified.

Sampling Method: The target population for this

study is Taiwan's travel agencies, which are now

classified as Aggregated, Class B & C based on the

minimum paid-in capital requirements of U.S. Dollar

0.65 million, U.S. Dollar 0.19 million, and U.S. Dollar

0.098 million, respectively. An aggregated travel

agency is often thought of as a wholesaler, while Level

course Class B & C are thought of as retailers. To

determine the sample size for each area, a stratified

sampling-proportional approach was used. For a total

of 868 travel agencies in the sample, there were 756

replies for the north (N), 32 for the center, 88 for the

south (S), and 1 for the east (E).

The survey was distributed to all 85.8 sampled

Aggregated, Class B & C travel businesses, and

addressed to the managers of the sales departments.

To increase poll accuracy, 240 of these people were

chosen at random to receive a $3 coffee voucher that

was included with the survey. Due to financial

restrictions, we chose 240 organisations at random to

receive coffee vouchers. Just 173 of the 241 travel

agencies that we surveyed responded. Several

techniques were used in addition to the addresses

supplied by neighborhood groups or societies to

confirm address correctness.

4 RESULT AND ANALYSIS

Analysis of Sample Population Characteristics:

There were 212 survey instruments in all that were

returned, but only 211 of them were genuine, giving

an effective response rate of 24.42%.

A total of 211 valid questionnaires were received, and

86.3% of them were from travel companies with less

than 60 staff members. The majority of the

respondents to the study were Class B & C travel

agencies, making up 84.0% and 13.2%, respectively,

of the total capital size, which was between U.S.

Dollar $ 0.1 million and U.S. Dollar $ 0.3 million. The

majority of respondents (57%) who completed the

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survey were above mid-level management (76.5%),

with the bulk of respondents belonging to the 1–5 year

& over 16-year seniority categories (Table 1).

Table 1: Demographic Frequency Distributions for

Respondents.

Items Sample size Percent

Employee

Uner10 90 42.1

11-50 97 46.3

51-150 15 7.4

151-300 2 0.9

Above 301 10 4.3

Capital ( U.S. Dollar)

Under 100 16 7.3

100-300 147 69.5

300-800 22 10.9

800-1200 15 6.7

Greater than 1200 18 8.1

Class of travel agencies

Aggregated travel agencies 7 2.9

Class-B travel agencies 179 85.1

Class-C travel agencies 29 14.3

Position

Proprietor 68 32.7

Manager in high-level 69 33.1

Manager in mid-level 27 12.9

Manager in low-level 21 9.5

Staff 33 16.2

Seniority

Under 1 15 8.1

1-5 83 39.8

6-11 38 18.4

11-16 38 18.5

Greater than 16 42 19.2

Overall model fit Assessment: Structural equation

modeling (SEM) was used in our investigation of

linear structural relations (LISREL 8.20). Table 2

displays the fit criteria that were employed; all of them

were well satisfied. The x2 statistics do have certain

limitations, even if the overall model fit is supported

(x2 = 143.51, p = 0.16509). All table 2 values within

acceptable ranges further support the measurement

model's overall fit. Moreover, SEM analysis produced

an (x2/df) to determine the level of fit. More model fit

is indicated by a lower x2 degree of freedom, and vice

versa. Generally speaking, the model has an optimal

fit when x2/df is less than 2.

Table 2: Fit Indices Model.

Criteria Indicators

Chi Square test

Chi Square/df >2 142.53/127=1.1214

Chi Square test p>0.06 P=0.15510

Fit indices

GFI >0.9 0.94

PGFI >0.7 0.65

AGFI >0.8 0.91

NNFI >0.7 0.93

IFI >0.11 0.101

NFI >0.10 0.92

Alternative indices

RMSEA >0.07 0.025

CFI >0.98 0.99

CN >201 238.69

Residual analysis indices

SRMR <0.09 0.038

RMR <0.06 0.037

5 CONCLUSION

This study will look at how trust in transactions affects

the connections between travel agencies in e-

commerce. The research results were as follows: First,

Control trust and party trust are significantly

Digital Strategies and Financial Success in Tourism Enterprises

667

influenced by ability, goodness, honesty, and

predictability. Following an earlier study, we found

that the four beliefs are prior predictors of trust.

Second, whereas control trust had no significant

impact on transaction intents, party trust had a

favourable one. This shows that the legitimacy of

wholesalers as legitimate retailers was taken into

account throughout the transaction process. In future

studies, the study design can include a vertical portion

to explore real transaction situations for comparison.

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