Cognitive Analysis of E-Commerce Adoption Factors in Small and

Medium Enterprises

Mahesh Singh

, Manoj Kumar Rao

, Manoj B Pandey

and Amit Mishra

Swiss Business School, Al Tareeqah Management Studies, U.A.E.

J. D. College of Engineering and Management, India

Jhulelal Institute of Technology, India

University of Lucknow, India

Keywords: E-Commerce, Small and Medium-Sized Enterprises (SMEs), E-Business Internet Technologies.

Abstract: Small and medium-sized businesses (SMEs) are crucial to most economies, especially those in emerging

nations. This study's goal is to combine these elements and assess their degree of impact on adoption decision-

making, whether it was favourable or bad from the adopter's viewpoint. In a survey addressed to SMEs, 26

criteria that have been utilised in many adoption models as variables in the literature were given impartially

without being labelled incentives or hurdles. The impact of these variables on respondents' choices to adopt

e-commerce/e-business Internet technologies (EEIT) was rated by respondents. According to the study's

conclusions, variables are seen differently by adopters, those who plan to adopt, and those who do not. These

findings need to provide the foundation for the adopting models' greater exact use of these factors.

1 INTRODUCTION

In the past decade, the academic community has

increasingly scrutinised the adoption and application

of Electronic Enterprise Information Technology

(EEIT) within the realm of small and medium-sized

enterprises (SMEs). Researchers like Ghouchani and

colleagues (2019) have highlighted the

transformative potential of EEIT for SMEs,

suggesting that it could potentially mitigate several

challenges faced by these entities, including

constraints related to size, financial limitations,

geographical isolation, and market access. The

predominant methodologies employed in these

studies have been surveys, interviews, and case

studies, each aiming to dissect and understand the

nuances of EEIT implementation. A prominent focus

has been on identifying and categorising various

factors that either promote (incentives) or hinder

(barriers) the uptake and effective use of EEIT in

these business settings. This thematic investigation

has been crucial in distinguishing the elements that

either propel or impede technological integration

within SMEs.

Recent scholarly work, such as that by Ufua et al.

(2020) and Gabinete et al. (2022), has aimed to

systematically categorise these factors into incentives

and barriers, examining their impact on EEIT

adoption. These studies have laid the groundwork for

developing a coherent framework, yet they also

underscore the disparities in how these factors are

named, grouped, and defined across different studies.

Such inconsistencies signify a gap in the literature,

prompting a need for a more unified and robust

understanding of what drives or deters EEIT adoption

among SMEs. Addressing this gap, the current

investigation seeks to establish a more standardized

classification of these factors, enhancing the

academic and practical comprehension of EEIT

dynamics in the SME sector. Drawing upon

preliminary findings from a 2002 pilot study by Bajaj

et al., which primarily identified perceived barriers to

EEIT utilisation, this study extends the inquiry to

explore both the positive and negative influences of

identified variables. Through a meticulous analysis

and reevaluation of these elements, the study aims to

provide insightful guidance on how SMEs perceive

and interact with EEIT, thus fostering a clearer

pathway for future research and practical

implementations within this technological domain.

Singh, M., Rao, M., Pandey, M. and Mishra, A.

Cognitive Analysis of E-Commerce Adoption Factors in Small and Medium Enterprises.

DOI: 10.5220/0012873200003882

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 497-500

ISBN: 978-989-758-723-8

497

2 LITERATURE SURVEY

Ghouchani, et al., 2019 shows how competencies

underpin the processes of developing international e-

commerce. The goal of the article is to pinpoint

variables affecting importance e-commerce in Saudi.

Moreover, it investigates how these elements

affect E-customer loyalty (Ayed 2022). Hou et al.

2022 investigate the variables that may have an

impact on viewers' desire to watch live streaming for

extended periods of time. Waseem et al. 2018

mentions that the goal of this research is to create an

integrative model based on several variables that

might influence the development of e-commerce in

any nation.

Daskalakis et al. 2022 aims to carry out an

academic writing analysis of well-known fusion-

based approaches that may help e-commerce settings

address common issues and meet their requirement to

make more precise and superior business judgements.

3 RESEARCH METHODOLOGY

The formulation of a lengthy list of factors identified

in the writings as key incentives or obstacles to the

using and implementing technology, as well as a

comprehensive assessment of the literature, served as

the initial stage in the present research. Adopting

usage of EEIT by SMEs was the review's main

emphasis, but it wasn't the only one. Furthermore

taken into consideration were studies on the broad use

and utilisation of information and communication

technologies (ICTs). The review focused mostly, but

not exclusively, on SMEs' use and adoption of EEIT.

Data Collection: A postal survey instrument was

used to gather the data. The instrument's ultimate

design went through three phases of development.

First, a pilot investigation was finished. This

consisted of a straightforward 2-page survey that was

delivered at a local micro business convention. An

initial draught of the last document was produced and

delivered up to ten local small businesses. To clarify

respondents' comprehension the inquiries and replies,

three of these firms were interviewed again. These

conversations helped revise and clarify the last

survey.

Optical scan forms were used to print the survey,

allowing for easier and more precise data entry.

Kentucky's 49 Appalachian counties' small and

medium companies were surveyed. These counties

are in a rural region with poor infrastructure and

poverty. All Eastern Kentucky SMEs, excluding

petrol stations and franchised eateries, were counted.

2,156 eligible enterprises represented 96% of the

region's businesses. SMEs had 500 or less workers.

The US Small Business Administration's most

frequent small business definition is 500 or less

workers. Yet, only 10 enterprises had over 100

workers in the final findings 180 of 2,156 surveys

were returned due to improper addresses. 1,976

presumed provided to area companies. 107

questionnaires with responses were received, a 5.41%

response rate. 5 of the 107 questionnaires from

organisations above 500 workers were omitted from

the data sample. This results in 102 small enterprises

being included in the final sample. The research

studied many e-business and Internet technologies,

but this article will concentrate on corporate website

adoption and ecommerce. Forty-seven (46%)

respondents indicated their website domain name.

This matches US statistics showing 50% of small

firms possess a webpage. The remainder 55 (54%)

enterprises, 36.8% intend to construct a website

within one year, 12.3% Just 7.02% will not create a

company website during the next two to five years,

while 44% are considering it but have no concrete

plans. Just 10 of the 47 respondents with a website

make any sales online, and only 3 make a

considerable amount (over 10%).

4 RESULT AND ANALYSIS

This section covers the findings and analysis of

information from two survey parts. Characteristics of

instrument respondents, which provide information

about the survey participants and their companies,

and respondents' assessments of the aspects that,

affected their judgements, either as motivators or

obstacles to adopting EEIT. Some information

respondents to usage of EEIT is provided, and more

information is provided on respondents' use of

websites. In specifically, the firms are divided in

order to further divide people examination of

perceived incentives and obstacles based on the

degree to which each company under study has

adopted a website.

Respondent demographics: Based on the

number of workers, the size of the responding

organisations was broken down as follows: fewer

than 10 workers (22 respondents, or 21.57%); 11–50

workers (47 survey participants, or 46%); 51–100

employees (20 survey takers, or 19.61%); and 101–

500 staff (10 respondents, or 9.80%).For those who

responded to the polls on behalf of their

organizations, over 63% of respondents said they

were personally in charge of the IT choices and

resources in their respective enterprises. A further

25.5% claim to have a direct influence on IT choices

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498

and resources. The survey results were initially

gathered and statistical analyses conducted to

discover population-wide incentives or impediments.

Further evaluations were done to see how specific

characteristics could affect adoption. Adopters and

non-adopters of websites are first compared.

Table 1: ANOVA Analysis on Website Adopters and Non-

Adopters.

Factor df f Between

group

significan

Adopter

Non-

adopter

Competitive

ressure

87 13.767 0.002 **0.745 0

Prior

experience

85 9.813 0.004 0.284 *-0.500

Partners/vendo

86 7.184 0.0011 **0.487 -0.164

Reliability 88 6.267 0.015 *0.537 -0.18

Technical

expertise

84 6.157 0.016 **0.547 *-0.024

Capital 89 5.897 0.018 0.047 *0.645

Models 91 4.554 0.035 0.458 -0.254

Table 1 depicts the ANOVA analysis on substantial

website adopters' and non-adopters'. A factor is

significant in that group if it is significant at the 0.05

level or the 0.001 level. It is noteworthy to notice that

all variables that shown a statistically significant

difference between the groups of either Adopters

against non-adopters, or Adopters versus non-

adopters, save for the aspect of competitive pressure,

DF stands for proportions of freedom.

Table 2: Results of ANOVA Analysis on Website

Adaptation.

Factor df f Between

group

nificance

Adopters Non-

adopters

Intend to

Ado

Will not

ado

Value 47 14.523 0.002 *0.721 -0.181

EC technology 47 5.025 0.335 0.334 -0.228

Need 45 8.274 0.005 **0.645 -0.185

Innovativeness 46 4.519 0.041 **0.801 -0.143

Market 49 4.421 0.042 0.131 *-0.697

Table 2 depicts the results of ANOVA analysis on

website adaptation. It denotes a factor important in

that subgroup at the 0.05 level; ** denotes a variable

important in that subgroup at the 0.001 level.

5 DISCUSSION

Several of the characteristics reported in the literature

are combined into 26 neutral factors in this research.

Despite the assumption that all factors would be

neutral, a t-test showed that 16 of the 26 various ﬁelds

had a measurable influence on the decision to adopt.

The price of EEIT was the only constant factor that

emerged as important across all five adoption groups.

These findings seem to indicate that maybe creativity

assessments should be given more weight in the

adoption hypothesis, but further study is needed to see

if this finding of assessing inventiveness would hold

true if it were examined objectively rather than

subjectively.

6 CONCLUSION

The key flaw in the population of SMEs is the focus

of this research that was sampled was restricted to

Kentucky, a state in the United States. The outcome

is unknown. to what degree Those taking part include

typical of all small and medium-sized firms. However

the attendee demographics offer no proof that does

not conform to the usual traits of small and medium-

sized businesses. Therefore, while analysing research

findings, this restriction should be taken into

consideration. The research also successfully

highlights the difficulty in forming hypotheses

regarding the use of these components of the

statistical analysis of adoption studies. Just one of the

26 variables, cost, was consistently significant across

all five groups, and many other factors are only

significant for one or two of the five groups.

The perceptions of the incentives and obstacles

that could get in the path of website adopters, those

who wish to embrace it, and those who have no

interest in doing so vary. Several of the 16 criteria that

this study identified as being relevant got less focus

in earlier research and may need additional effort in

next research on EEIT implementation in small and

medium firms. Government, innovation, and industry

models are three of the incentives, while security is

one of the barriers. According to the research, cost

and resource constraints were the biggest obstacles to

the implementation of EEIT.

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