International Journal of Technology Management and Information System (IJTMIS) eISSN: 2710-6268 [Vol. 4 No. 1 March 2022]
Journal website: http://myjms.mohe.gov.my/index.php/ijtmis
CORRELATIONAL ANALYSIS TO ANALYSE THE FACTORS OF TECHNOLOGICAL INFRASTRUCTURE AND DIGITAL LITERACY AGAINST INVOLVEMENT OF RURAL MUSLIM
SMES’ IN E-COMMERCE DURING PANDEMIC COVID-19 (CASE STUDY OF PERLIS, MALAYSIA)
Mohd. Zaki Shahabuddin1*, Rozana Mohd Jamil2, Izwan Nurli Mat Bistaman3, Fatimah Noni Muhamad4 and Nor Izham Subri5
1 2 3 4 5 Jabatan Teknologi Maklumat, Fakulti Perniagaan dan Sains Pengurusan, Kolej Universiti Islam Perlis,
MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 18 February 2022 Revised date : 6 March 2022 Accepted date : 20 March 2022 Published date : 30 March 2022 To cite this document:
Shahabuddin, M. Z., Mohd Jamil, R., Mat Bistaman, I. N., Muhamad, F. N., &
Subri, N. I. (2021).
CORRELATIONAL ANALYSIS TO ANALYSE THE FACTORS OF TECHNOLOGICAL
INFRASTRUCTURE AND DIGITAL LITERACY AGAINST
INVOLVEMENT OF RURAL MUSLIM SMES’ IN E-COMMERCE DURING PANDEMIC COVID-19 (CASE STUDY OF PERLIS,
MALAYSIA). International Journal of Technology Management and
Information System, 4(1), 16-29.
Abstract: During a period of pandemic Covid-19, trend of people go for online shopping shows remarkable growth where millions of consumers around the world looking for products, services and entertainment from Internet.
Electronic commerce or E-commerce, not only involve the selling and buying online, but comprises every part of business transaction carried out over the Internet and its related technology. Studies demonstrate factors such as telecommunication infrastructure, high-speed Internet access, smart-phones and mobile devices ownership, and digital literacy to contribute for E-commerce growth.
However, are these factors can be associated to the involvement of E-commerce among Muslim’s SMEs from rural area? Hence, the study is to analyse two factors of technology infrastructure and digital literacy to the involvement of Muslim’s SMEs in E-commerce during the period of Covid-19 pandemic, and aiming to the Muslim’s SMEs in the state of Perlis, Malaysia. The study had adapting correlational analysis to measure two pair of variables of technological infrastructure, and digital literacy against participation of Muslim SMEs in E- commerce. Result shows the pair of variables had strong correlation only when moderating variable from demographic profiles are taken into consideration.
1. Introduction
Malaysia recorded first incident of Covid-19 in January 2020, with 22 imported cases detected.
More infections were triggered from local transmission resulting the cases to surge rapidly throughout the country (Salim et.al, 2020). A lock-down approach called Movement Control Order (MCO) was immediately initiated in the country to suppress the infection trajectory. The MCO has the ability to curb the infection dynamic is urgently required to assist the government on timely decisions, in other hand caused a major socio-economic disruption to majority of businesses through-out the country.
MCO had adversely affecting several key economic sectors including retail, services, hospitality and entertainment, where businesses had to cope with SOP’s restrictions, which affected their operations and revenue. Further, businesses also facing cost-push inflation due to increasing cost of raw materials due to shortage in goods and services.
2. Literature Review
The impact of Covid-19 on Malaysia’s economy can be seen from depreciation in our Malaysian Ringgit currency and declination of country’s gross domestic product. In March 2019, Malaysian currency is pegged at RM 4.08 against USD 1, and the value depreciated to RM 4.26 per 1 USD in July 2020, 3 months after the country imposed the MCO (Zainuddin & Shaharuddin, 2020).
The Department of Statistics, Malaysia (2020) conducts a study to more than 4000 businesses in the country to find the impact of pandemic. Overall, the report shows more than half of businesses not be able to survive for 2 months operation if they are running at normal capacity as before the pandemic time. Almost 70% of businesses informed they faced shortage of income due MCO lock-down and restrictions. Although, only 12% business firms still earned revenue through online sales or services, and less than 10% still relies on mortal-brick physical shops. Further, 70% of businesses reported they are now utilizing their savings to accommodate operating cost or working capital during MCO period of time (Source: Effects of COVID-19 on the Economy and Companies/Business Firms’ - Round 1).
However, Shahabuddin et al. (2021) spelled out even-though everything went wrong with pandemic, there still always be a silver lining for businesses as more people go for online shopping.
Hence, businesses must diversify their revenue and profitability by launching online or digital business platform in order to sustain themselves during the pandemic time.
Prior to Covid-19, world governments had nurturing E-commerce development program to rural areas for the purpose to reduce the rural-urban economic divide. The program led in other developing countries with large rural populations, such as Egypt, India and Vietnam, where they announced similar E-commerce expansion plans (MCIT National E-commerce Strategy-Ministry Keywords: Correlational analysis, technological infrastructure, digital literacy, e-commerce, covid-19.
growing number of case studies on highly successful E-commerce villages that have experienced rapid output growth by selling both agricultural and non-agricultural products to urban markets via online platforms. One of the most prominent examples is in China where the largest E- commerce platform, Taobao, had branded more than 3000 rural market places as ‘Taobao Villages’
based on their high concentration of online sales.
There are several challenges in development of rural E-commerce. Some challenges are common in general such as inadequacies in infrastructure, transport and logistics, issues in supply chains, problems and security related to payment system, issues in timeliness and quality of delivery, digital literacy and digital information asymmetry (Chatterjee, 2019).
E-commerce Infrastructure
E-commerce as an exemplary combination of all technological infrastructure namely the communication networks, computer hardware and software, as well as technological standards, security, apps design, database design and operation (Laudon, 2017). According to Akelloh et al (2017), E-commerce is the purchasing, selling, and exchanging of goods and services over the Internet through which transactions or terms of sale are performed electronically. With intensive use of technologies, it enables buyers to purchase or sellers to access business market globally, and the needs for adequate ICT infrastructure can considered as challenging requirements for business or individuals to participate in E-commerce.
Digital Literature
E-commerce is a set of technologies, applications, and business processes that link business, consumers, communities, governments, private and public institutions for buying, selling, and delivering products and services over the Internet (Dave et al, 2019). E-commerce has advantages offered by technology that enables purchasers and sellers to reach their vendors at any part of the world without barrier. Those who enjoy the use of technology to transact over the Internet, basically require computers, knowledge, skills in ICT and access connection to Internet (Pirzada and Khan, 2013). Lack of awareness of the use, the knowledge and potential benefits of ICT can also hinder the growth of E-commerce (Kapurubandara and Lawson, 2008).
2.1 Problem Statement
Previous studies grasp the factors concerning behavior of E-commerce acceptance among stakeholders ranging the industries to the end-users. Backing by good communication infrastructure and solid digital knowledge, E-commerce transactions can be executed efficiently among parties involved. Foreseeing this, it has become a motivation for this study to further anticipate whether factors of technological infrastructure and digital literacy among the Muslim SMEs from least urban areas, whether it play significant impacts to involvement of Muslim SMEs in E-Commerce particularly during the period of Covid-19 pandemic.
In particular, the objectives of this research are:
1. To investigate the correlation of technological infrastructure and Muslim SMEs involvement in E-commerce during pandemic Covid-19.
2. To investigate the correlation between digital literacy acquired by Muslim SMEs towards their involvement in E-commerce during pandemic Covid-19.
3. Method
This research adapted the method of correlational analysis to investigate the two parameters of technology infrastructure and digital literacy against the involvement of Muslim SMEs in E- commerce. According to Neuman (2003a), correlational analysis is non-experimental study to measure variables, recognises and assesses the statistical relationship between two variables.
Figure 1 shows the study’s methodology approach stirred by correlational analysis method as proposed in Neuman (2003a).
Figure 1: Correlational Analysis Method Correlate two or more variables
Conduct data collection at one time
Analyze all responses as a single group Obtain the scores for each individual in the group - one for each variable
Reporting the correlation statistic
3.1 Theoretical Framework
Hypothetically, this research studies a correlation between technological infrastructure and E- commerce involvement, and correlational between digital literacy and E-commerce involvement of Muslim SMEs. The demographic profile might be used as moderating variable to enhance the correlation of both group of variables as depicted in Figure 2.
3.1.1 Sampling and Site of Study
The study was conducted in the state of Perlis, randomly selecting the Muslim SMEs regardless their age and types of business owned. State of Perlis, was chosen because it meets the criteria for becoming least developed area where half of state population currently staying in rural kampong as depicted by Jabatan Perangkaan Malaysia (Source: https://www.dosm.gov.my).
3.1.2 Procedures
A set of survey questionnaire was developed and personally administered according to the objectives of the research. The survey questionnaire was developed in a form of non-parametric nominal and ordinal closed ended questions. There are divided into 4 parts of the followings:
• Demographics – consists of nominal type of queries asking for respondents’ gender, location of business, business sector, age of business, and the usage and frequency of E-commerce applications.
• The situation of Covid-19 – consists of ordinal questions to understand how much Covid-19 has affecting the business in terms of business income, operations, and customers turn over during the pandemic time.
• IT infrastructure and communication – consists of thirty questions inspired from technological infrastructure dimensions of Tapscott and Caston (1993). The ordinal closed ended questions are randomly designed to understand the psychological status reflecting businesses’ interest in
Figure 2: Correlational Theoretical Framework Demographic Profile
- Business Type - Business Sector - Business Market - Years of Operation - Application Used in
Business Technological Infrastructure
Digital Literacy
Involvement in E-Commerce
Moderating Variables
Dependent Variable Independent Variables
IT, both important and personally relevant to their business. This section consists of adaptation to technology, networking and connectivity, and technology flexibility demonstrated by the businesses in direction to IT infrastructure.
• Digital literacy - consists of twenty ordinal closed ended questions adapted from Seven Domains of Digital Literacy model (SDDL), developed and validated by Kurtz and Peled (2016). The queries are developed to cover individual perception in retrieving, evaluating and processing digital information, how each individual manage the information gathered, and the individual’s awareness on integrity and responsibility in managing digital information.
Responses to questionnaire were collected within the period of two (2) months, where efforts are done to alert and monitor sampling responses from time to time.
4. Results and Discussion
Demographic Profile of the Respondents
There was successful return of 50 sets questionnaires. Even so, only 40 sets were usable due to 10 sets of incomplete questionnaires.
Table 1 summarise demographic profile of the participants. Majority of the Muslims SME owners in Perlis were males, 57.5% and females 42.5%. The business mostly focused at the main cities of Perlis which are Kangar (35%), Arau (17.5%), Chuping (12.5%), Kuala Perlis and Beseri (10%), Padang Besar 5%, and other parts of Perlis of Pauh, Wang Kelian, and Santan share the same percentage of 2.5%.
Sole-proprietorship is a simplest form of business in which the owner owns the entire business and has right to receive all the profits (Miller, 2021). Because of its simplicity, Muslim SMEs in Perlis prefer to have sole proprietorship business as compared to partnership and private limited with percentages of 77.5%, 15% and 7.5% respectively.
Foods and beverages (45%), clothing (15%), groceries (10%) and suppliers (10%) are the leading business sectors engaged among Muslim SMEs, followed by homestay and engineering, manufacturing and construction (5%). The responses were merely form second and third sector of the business in Perlis, Malaysia.
It has been more than one-year Covid-19 hit Malaysia. The rise of pandemic shows the emergence of new Muslim SMEs about 35%, in which years of operation is below one year. This confirms that the new SMEs start their business during the pandemic. There are about 30% of Muslim SMEs having more than 5 years of operation still managed to maintain their operation during the pandemic.
Majority of the Muslim SMEs in Perlis are more focused on their business within the state (57.5%), compared to within country (32.5%) and around the neighbouring states (10%).
Table 1: Demographic Profile of the Respondents
Category Frequency Percentage (%)
Gender Male
Female
23 17
57.5%
42.5%
Business’ Location Arau
Chuping Kangar Kuala Perlis Padang Besar Pauh
Wang Kelian Beseri Santan
Other Parts of Perlis
7 5 14
4 2 1 1 4 1 1
17.5%
12.5%
35.0%
10%
5%
2.5%
2.5%
10%
2.5%
2.5%
Business Type Private Limited Partnership Sole Proprietorship
3 6 31
7.5%
15%
77.5%
Business’ Sector
Food and Beverages Groceries
Clothing (Bundle Store, Tailoring) Laundry and Car Wash
Supplier (Healthy Product, Air-conditioner, Cuckoo and Coway) Homestay
Engineering, Manufacturing, Construction Financial
Printing
Technology and Communications
18 4 6 1 4 2 2 1 1 1
45%
10%
15%
2.5%
10%
5%
5%
2.5%
2.5%
2.5%
Years of Operation Below 1 year 1 – 3 years 3 – 5 years More than 5 years
14 6 8 12
35%
15%
20%
30%
Business Market Within country Within state
Around the neighbouring state
13 23 4
32.5%
57.5%
10%
Number of E-Commerce Applications Used in Business 2
3 4 5 6 7 8 9
3 7 6 8 9 2 4 1
7.5%
17.5%
15%
20%
22.5%
5%
10%
2.5%
Figure 3 depicts the E-commerce applications used by Muslim SMEs in their business. 87.5%
SMEs chose to use Messenger, WhatsApp, and email to advertise or market their businesses, followed by using social media marketing like Facebook, Instagram, etc (70%), Internet banking (52.5%), Internet search (40%) and E-commerce site (35%). Only 20% of the SMEs used e- booking and 10% have their own website. Table 1 also shows that 22.5% of the SMEs use at most 9 applications to market their business.
Figure 3: E-Commerce Applications Used in Business
The Impact of Pandemic to Business
Figure 4 shows the impact of Covid-19 pandemic towards the business. Generally, SMEs not directly affected due to relativeness to food and beverage (F&B) business, which contributes 45%
out of total samplings. During MCO, F&B is considered the essential sector allowed to operate under strict SOPs. It means the business still able to operate under circumstances where there will be no dines-in are allowed, and foods must be taken-away by all customers. Hence, the approach for take away has opted for more usage of technology applications.
More than 55% SMEs believed their businesses are significantly affected by the pandemic where there are facing a decline of their business income due to MCO.
The major influences that SMEs re facing are decline number of customers (75.6%), and not able to fully operated during MCO (55.6%).
0 10 20 30 40 50 60 70 80 90 100
Internet Search Internet Banking E-Mail, Whatsapp, Messenger Own Website E-commerce (Lazada, Shopee, etc) Media Sosial Marketing E-booking / Orders Job/Contract/Tender Offer Others
Percentage
E-Commerce Applications Used in Business
Figure 4: Impact of Pandemic to Business (from the least, R1 to mostly affected, R5)
Reliability and Normality Test
Cronbach’s Alpha is a statistic usually used to demonstrate the test and scales that been constructed closely related as a group. Table 2 shows the summarisation of reliability of dependent variable and independent variables. Values of Cronbach’s Alpha for independent variable 1 and independent variable 2 is more than 0.7, which shows high reliability while dependent variable and moderating variable 3 with Cronbach’s Alpha of 0.457 and 0.407 respectively, is consider moderate reliability. In Table 3, Shapiro-Wilk Test indicates that the collected data satisfy the normality test with significance value greater than 0.01 significance level.
Table 2: Result of Reliability Test: Cronbach’s Alpha
Variables Construct Cronbach’s Alpha Number of Item Dependent Involvement in E-
commerce
0.457 9
Independent 1 Technological Infrastructure
0.893 5
Independent 2 Digital Literacy 0.914 6
Moderating Demographic Profile 0.407 5
Table 3: Normality Test
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Involvement in E-commerce .207 40 .000 .926 40 .012
Correlation Analysis
Table 4: Correlation of the Model
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .817 .667 .595 .18776
Pearson’s correlation was executed to determine the coefficient of correlation of dependent variable, against the independent variables. Ratner (2009) describe the strength of the correlation coefficient is strong if the value of correlation coefficient lies in between 0.7 to 1.
Based on the result in Table 4, the coefficient of correlation between dependent variable and all independent variables is 0.817. The values show that independent variables have strong positive
2 2
13
11 12
0 5 10 15
R1 R2 R3 R4 R5
The Impact of Pandemic to Business
linear relationship with the dependent variable. This implies that, the increase in both independent variables have strong effect on dependent variable. Coefficient of determination or adjusted R- square is 0.595. The value indicates 59.5% of variation in dependent variable can be determined by the independent variables. The remaining 40.5% of variation in dependent variable is due to other factors.
Table 5 shows the correlation between each variable. The correlation coefficient for Technological Infrastructure (TI) shows strong positive correlation with the Involvement in E-commerce (I) with correlation coefficient, r = 0.7. Digital Literacy (DL) shows moderate correlation with correlation coefficient, r = 0.562 and all-demographic variables (BT, BS, YP, BM and AU) show weak correlation as the value falls below 0.5 against the dependent variable.
Table 5: Correlation Between Variables
Correlations
I TI DL BT BS YP BM AU
Pearson Correlation
I 1.000 .700 .562 .132 .050 .233 .409 .444 TI .700 1.000 .701 .209 .263 -.173 .492 .501 DL .562 .701 1.000 .003 .152 -.105 .251 .303 BT .132 .209 .003 1.000 -.005 -.056 .439 .209 BS .050 .263 .152 -.005 1.000 -.072 .415 .210 YP .233 -.173 -.105 -.056 -.072 1.000 -.064 .000 BM .409 .492 .251 .439 .415 -.064 1.000 .455 AU .444 .501 .303 .209 .210 .000 .455 1.000 Sig. (1-tailed) I . .000 .000 .208 .381 .074 .004 .002 TI .000 . .000 .098 .050 .144 .001 .001 DL .000 .000 . .493 .174 .259 .059 .029 BT .208 .493 .098 . .488 .365 .002 .098 BS .381 .174 .050 .488 . .330 .004 .097 YP .074 .259 .144 .365 .330 . .347 .500 BM .004 .059 .001 .002 .004 .347 . .002 AU .002 .029 .001 .098 .097 .500 .002 .
N I 40 40 40 40 40 40 40 40
DL 40 40 40 40 40 40 40 40
TI 40 40 40 40 40 40 40 40
BT 40 40 40 40 40 40 40 40
BS 40 40 40 40 40 40 40 40
YP 40 40 40 40 40 40 40 40
BM 40 40 40 40 40 40 40 40
AU 40 40 40 40 40 40 40 40
DL = Digital Literacy BT = Business Type
BM = Business Market AU = Applications Used
Multiple Linear Regression Analysis
Table 6: ANOVA
Model Sum of Squares df Mean Square F Sig.
1 Regression 2.264 7 .323 9.175 .000
Residual 1.128 32 .035
Total 3.392 39
Analysis of variance test (ANOVA) helps to assess whether the coefficients of independent variables are the same for all categories. The significance value in the table is 0.000 is smaller than the significance level, 0.01. Thus, there is sufficient evidence the parameters are not the same for all categories. This can be supported by values of coefficients given in Table 7.
From Table 7, the unstandardized coefficient linear equation is formulated as:
Involvement in E-commerce = 0.799 + 0.0413 Technological Infrastructure + 0.116 Digital Literacy – 0.028 Business Type – 0.023 Business Sector + 0.095 Years of Operation + 0.056 Business Market + 0.012 Applications Used
The findings in Table 7, shows that Technological Infrastructure is statistically significant predictor of Involvement of Muslim SMEs with significance value 0.001. For every one (1)-unit increase on Technological Infrastructure, there is a predicted increase of 0.413 the Involvement in E-commerce, when other variables remain constant.
Years of Operation is also statistically significant predictor of Involvement of Muslim SMEs with significance value 0.002. For every one (1)-unit increase on Years of Operation, there is a predicted increase of 0.095 the Involvement in E-commerce, when other variables remain constant.
In spite of this, Type of Business, Business Sector, Business Market, Applications Used and Digital Literacy are not statistically significant predictors in the model as the significance value is greater than 0.01 significance level. The expected value of Involvement in E-commerce will decrease by 0.028 and -0.023 for each unit of Type of Business and Business Sector respectively, when other variables remain constant for that particular variable. The expected value of Involvement in E-commerce will increase by 0.056, 0.12 and 0.116 for each unit of Business Market, Applications Used and Digital Literacy respectively, when other variables remain constant for that respective variable. One of the factors of the insignificant predictors is due to low sample size.
Table 7: Coefficients
Variables
Unstandardized
Coefficient Standardized
t Sig.
99% confidence Interval for B
Collinearity Statistics
B Std
Error B Lower
Bound
Upper Bound
Tolerance VIF
Constant .799 .262 3.046 .005 .081 1.517
Technological Infrastructure
.413 .117 .603 3.519 .001 .092 .735 .354 2.823
Digital Literacy
.116 .123 .139 .948 .350 -.219 .452 .483 2.070
Business Type -.028 .047 -.069 -.587 .561 -.157 .101 .743 1.346 Business
Sector
-.023 .013 -.195 -1.687 .101 -.059 .014 .777 1.287 Years of
Operation
.095 .029 .345 3.312 .002 .016 .173 .956 1.046
Business Market
.056 .046 .177 1.237 .225 -.068 .181 .508 1.969
Applications Used
.012 .020 .076 .613 .544 -.042 .067 .684 1.462
5. Conclusion
This research concludes that technological infrastructure is significantly indicate the Muslim SMEs Involvement in E-commerce within the state of Perlis. The result of technological infrastructure has strong linear correlation towards the involvement in E-commerce. The factor being significant argument to the study on involvement in E-commerce, and consistent with prior researchers’ findings by Levy and Powell, 2000; and Lyver and Lu, 2018. Years of operation from demographic profile also did plays an important role in determining the Muslim SMEs involvement in E-commerce. This is due to consideration majority of the businesses are established in the period when the pandemic declared in the country, and mostly the SMEs already opted for minimal E-commerce applications in conducting their businesses.
Nevertheless, business type, business sector, applications used and digital literacy are not statistically significant predictors in the model. Even though digital literacy has moderate positive linear correlation to the involvement in E-commerce, still the predictor is not strong enough to affect the whole model of Involvement in E-commerce. One of the factors of the insignificant predictors is due to low sample size.
5.1 Limitations and Recommendations
There is a limitation encountered in this study. The respondents may have biased towards the questionnaires provided; they may have answered the questionnaire based on their personal perceptions on the issues instead of their observation on the scenarios their business might facing during the pandemic time. This is because most of the responses of study were obtained from small sector of businesses that belongs to Muslim SMEs in Perlis, Malaysia. Therefore, it is recommended for more devoted efforts in distributing the questionnaire to the whole sectors of
6. Acknowledgement
This research work is fully sponsored and supported by Kolej Universiti Islam Perlis (KUIPs), Malaysia under the initiative of Short-Term Grant (STG) scheme, project number STG-019 awarded in the year 2021.
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