Factors Influencing the Adoption of E-Marketing in the Tourism Industry by SMEs in Developing Countries Based on UTAUT Model ()
1. Introduction
This research paper has five sections. Section one covers the introduction to the paper and information on the background of the study. The statement of the problem is also given in this section. Section two outlines some various literature done by different scholars on the subject matter, identifying findings and gaps. Section two goes further to introduce the theoretical framework and conceptual framework developed by the author. The methodology that was employed to carry out the research design, population, data collection methods, techniques and analysis is in section three. Section four presents the results and analysis and is followed by the conclusion and recommendation in section five. The digital economy has transformed the way businesses operate worldwide as mentioned, and SMEs in Zambia are not exempted from this trend. In Zambia, SMEs are a critical component of the economy, accounting for over 60% of employment opportunities and 90% of total businesses in the country (Daka, 2022) and contribute significantly to the country’s GDP. However, many SMEs in Zambia face challenges in accessing markets, which hinders their growth and competitiveness. Digital or electronic marketing presents an opportunity for SMEs to reach a broader market and overcome these challenges. Further, the advent of the internet and social media has presented a new way of reaching customers. Electronic marketing or e-marketing is about making known of products and services to potential customers through the use of Information Communication Technology tools such as mobile phones, computers, the internet, among others. E-Marketing or electronic marketing is the application of marketing principles and techniques via electronic media and more specifically the internet (Mooya & Phiri, 2021) .
E-marketing is a rising aspect of electronic commerce, e-commerce being any economic or business activity that uses its utilities based on Information and Communication Technology (ICT) to enable the buying and selling of products and services and thus facilitating the transaction of business activities between and among businesses, individuals, governments, or other organizations. Mapeshoane and Pather (2016) identify e-commerce as undertaking personal and commercial activities by computer and telecommunications networks in both intra-organisational electronic messaging and information management. The wide variety of activities includes information exchange and data exchanges between two or more parties conducting business transactions over the internet in the form of products and services, as well as payments.
E-marketing, also referred to as digital marketing or internet marketing, involves the utilization of digital platforms such as social media, email, search engines, and websites to promote products and services (Chaffey et al., 2019) . It has gained significant popularity among businesses of all sizes due to its cost-effectiveness, wide reach, and the ability to track and measure results (Chaffey et al., 2019) .
Small and Medium-sized Enterprises (SMEs) play a crucial role in driving economic growth in Zambia, contributing to job creation, income generation, and poverty reduction (World Bank, 2022). In 2019, the travel and tourism sector’s contribution to the GDP in Zambia was 7.7% and provided close to 489,700 direct and indirect jobs according to Country Summary Report: Zambia (2022) .
However, many SMEs in Zambia including those in the tourism industry face challenges in adopting and implementing e-marketing strategies due to factors such as limited digital skills, resource constraints, and inadequate infrastructure (Tembo, 2019) .
Electronic media has evolved into an integral part of today’s global culture. This global trend also stands true in Zambia where the substantial shift towards mobile technology has been spearheaded by an increased interest in social media. One of the benefits of using electronic media is gaining access to the market without physically being present. Some SMEs in the tourism sector in Zambia have adopted e-marketing or internet marketing which uses digital channels such as social media, email, search engines, and websites to promote products and services. E-marketing provides SMEs in the tourism sector an opportunity to carry out their business regardless of the restrictions and disturbances that have come with the COVID-19 pandemic and beyond it.
The use of the internet has led to the emergence of e-marketing (Jaas, 2022) and its use is growing at a rapid rate. The growth has been additionally facilitated by globalization as well as cultural change towards the use of technology. It has, however, been noted that in developing countries the rate at which technological usage in daily life including buying and selling of goods and services is slow. Tembo (2019) alludes to the fact that while tourism industry has been one of the first adopters of e-marketing due to new technologies, the implementation has been slow in many aspects. Businesses are still sticking to traditional marketing methods such as the use radio, television and print media for marketing instead of using e-marketing. Here, thus, lies a fundamental problem-despite the benefits of digital marketing such as being cheaper and faster than traditional marketing SMEs in the tourism sector in Zambia have been slow in adopting and using it. There is, therefore, need to understand the factors that affect the acceptance of electronic marketing. This case study will attempt to examine factors influencing the adoption of e-marketing as mode of marketing by SMEs in tourism industry in Zambia.
2. Literature Review
Table 1 below of the literature review matrix, summarizes some of the related
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Table 1. Literature review matrix (Source: Author, 2023).
works on factors that influence the acceptance and adoption of e-marketing. It also highlights some gaps noted in the literature.
Apart from the literature cited in the literature review matrix, other studies have been conducted to put forward the usage of e-marketing for SMEs by highlighting the positive effect on growth performance and competitiveness. Taiminen and Karjuluoto (2015) , elaborated that SMEs did not seem to use the digital tools to their full potential which did not let the SMEs have the maximum benefit, and this was because of the lack of knowledge within SMEs about e-marketing. The gap noted from this research was the non-usage of technology adoption models in its conceptual framework.
Mugobi and Mlozi (2020) conducted a study to assess the determinants of ICT adoption at UNESCO World Heritage Sites in Tanzania. Their findings showed that, perceived relative advantage, perceived less complexity, IT infrastructure and support skills, competitive pressure significantly influenced decision makers’ intention to adopt the technology in the country. The research found that the use of digital technology in e-marketing by SMEs is very low. In Tanzania most SMEs are not fully utilizing digital technology in e-marketing which has affected them in several ways.
On the other hand, Odimmega, Udegbunam, Ile, and Azu (2016) found that inadequate communication infrastructures, high internet connectivity expenses, and inadequate power supply were the main obstacles to e-marketing adoption by SMEs in the Nigerian context. In the same situation, Nkosana, Skinner, & Goodier (2016) revealed that set up costs, owners’ lack of IT knowledge and employees’ lack of IT skills were the challenges in the adoption and utilization of e-marketing for SMEs at KwaZulu Natal Midlands in South Africa. The aforementioned papers of Odimmega, Udegbunam, Ile, and Azu plus that of Nkosana, Skinner and Goodier were of a qualitative nature and like most of papers in this literature review did not utilize any technology adoption conceptual framework. It is envisioned that this research paper will to an extent bridge the gaps that have been noted in the reviewed literature.
2.1. Theoretical Framework
A study operates within a certain theoretical parameter. This entails that there should be a theory that has been espoused within which a study is developed. This provides as a guide as well as a scholarly investigation of what is being studied. A number of theoretical models have been proposed to facilitate the understanding of factors impacting the acceptance of information technologies (Davis, 1989) . These theories have been utilized to in order to have a deep understanding of the theoretical aspect of the use of information communication technology in customer service delivery. The following part presents such theories.
2.2. Technology Acceptance Model (TAM) and Unified TAM
TAM was developed by Davis (1986) to explain the user adoption of technology in organizations. TAM posits that two factors, perceived usefulness and perceived ease of use, are the two main determinants of system usage in organizations (Taylor & Todd, 1995; Davis, 1989) . In TAM, Perceived Usefulness (PU) is defined as the degree to which an individual believes that using a particular system would enhance his or her job performance whereas, Perceived Ease of Use (PEOU) is the degree to which an individual believes that using a particular system would be free of physical and mental effort (Davis, 1989) . Several studies have been conducted to replicate and extend TAM to determine factors affecting technology adoption in organizations. Venkatesh and Davis (2000) applied TAM in a longitudinal study to include social influence processes and cognitive instrument processes. TAM was also tested by Grandon and Pearson (2004) in the small business context in the USA. They identified four factors that influence electronic commerce adoption: organizational readiness, external pressure, perceived ease of use, and perceived usefulness.
Though a popular model, TAM has been criticized for its generality and that it ignores certain personal behavioural factors (Taylor & Todd, 1995) , such as cultural and social influences that may be critical to understanding e-commerce adoption in SME contexts of developing countries. Further, the very fact of equating perceived usefulness to use has been seen as problematic in some literature. To perceive of a technology and its benefits that one has not used has been seen as problematic for TAM replication in the developing country context as the prevailing circumstances that exist in the developing countries are not necessarily the same as in developing countries (Avgerou, 2001) .
2.3. The Theory of Planned Behaviour (TPB)
Ajzen (1991) proposed the Theory of Planned Behaviour (TPB) from the social psychology background. TPB posits that there are three constructs that predict intention to use an innovation. These are attitude, subjective norm, and perceived behavioral control. Attitude is formed from cognitive beliefs and refers to “an individual’s positive or negative feeling (evaluative affect) about performing the target behavior” (Ajzen & Fishbein, 1975) . Subjective norm represents the social influences on behaviour and refers to the perception about whether others who are important to a person believe that he or she should engage in a particular behavior (Ajzen & Fishbein, 1975) .
2.4. The Unified Theory of Acceptance and Use of Technology (UTAUT) Model
Formulated by Venkatesh and others in “User acceptance of information technology (UTAUT): Toward a unified view”, UTAUT is an integration of various technology acceptance models which focus on different technology adoption issues and aims to explain user intentions to use an information system and subsequent usage behavior. The UTAUT model is widely used in the information and information technology industry. Venkatesh et al. (2003) compared the eight models from the previous research on information technology acceptance, including the technical adaptation model (TAM), the innovation diffusion theory (IDT), the theory of reasoned action (TRA), the theory of planned behaviour (TPB), the motivational model (MM), a model of combining TAM and TPB (C-TAM-TPB), the model of PC utilization (MPCU) and the social cognitive theory (SCT).
2.5. Conceptual Framework
1) Dependent Variables
The dependent variable is acceptance of electronic marketing (e-marketing).
2) Independent Variables
UTAUT divides variables into two types by reintegrating the previous models. The first type is the four core variables, including performance expectancy, effort expectancy, social influence and facilitating conditions. The second type is the moderating variables that have significant impacts on core variables, namely gender, age, voluntariness, and experience (Venkatesh et al., 2003) . Figure 1 is a depiction of the author’s proposed conceptual framework to guide this study, based on the UTAUT model.
2.6. Hypothesis
H1: There is a positive relationship between performance expectancy and the use of e-marketing.
H2: There is a positive relationship between effort expectancy and the use of e-marketing.
H3: There is a positive relationship between social influence and the use of e-marketing.
H4: There is a positive relationship between facilitating conditions and the use of e-marketing.
H5: There is a positive relationship between behavioural intention and the use of e-marketing.
3. Research Methodology
3.1. Research Design
The research design applied in this research was the descriptive research design and the mixed methodology was used. The quantitative aspect involved data collection through questionnaires. SPSS was employed to obtain results and draw conclusions on the research objectives.
3.2. Sample Size
The sample size was drawn from the target population which in this case are SMEs in the tourism industry, particularly those in the accommodation sector of hotels and lodges. The estimated population of hotel/lodge employees and owners in Livingstone is 1527 and the taro Yamane formula was used with 95% precision.
A response rate of 90% was achieved as 286 respondents returned full and correctly filled in questionnaires.
3.3. Sampling Technique
Data was collected using questionnaires and an interview guide. After collecting data, the first stage of data analysis was a descriptive one showing responses to individual questions in form of tables, graphs or percentages. This meant that data collected was to be partly descriptive and quantitative depending on the questions being closed or open-ended questions. The data needed to be processed to make it useful and meaningful. The data that was collected was examined and checked for completeness and clarity. To guarantee the content’s validity, the investigator conducted a pilot test of the questionnaires with 33 randomly chosen participants from the Livingstone area. This aimed to assess certain questionnaire parameters, including the clarity of question structuring, ease of navigation and identification of spelling errors, if any. Additionally, the questionnaire was distributed among individuals within the marketing domain, particularly those associated with the Zambia Tourism Agency. This approach sought to obtain a more comprehensive perspective on how respondents would perceive the questionnaire when it is formally administered in the final study.
Quantitative data was analyzed using inferential statistics which are Correlation, Regression and ANOVA analysis while qualitative data was analyzed by being transferred into quantitative data and analyzed by the help of Microsoft excel and SPSS in accordance with the objectives of the study. Quantitative analysis techniques such as graphs, charts and statistics allowed the research to explore, present, describe and examine relationships and trends within the data.
4. Results and Discussions
The analysis was performed using inferential statistic mathematical tools and the Statistical Package for Social Scientists commonly known by its abbreviation as SPSS. The five variables under observation were: Performance Expectancy, Effort Expectancy, Social Influence, facilitating conditions and Behavioral Intentions were used to assess if they are influence the actual usage of e-marketing in the tourism industry.
4.1. Demographic Information
Gender under demographic information revealed that 192 out of 286 respondents are male, representing 67 percent of the total respondents and 94 out of 286 respondents are female representing 33 percent of the total respondents as shown in Table 2 and Figure 2.
4.2. Age Distribution of the Respondents
The most frequent age among the respondents is between 41 years to 50 years with the frequency of 95 respondents representing 33.2 percent and the lowest frequent age is 50 years and above representing 7.3 percent of the total respondents. The information is indicated below is from the SPSS output (see Table 3 and Figure 3).
4.3. Level of Education
Analysis of the level of education of the respondents indicates that degree holders constitute the most frequent respondents with 113 out of 286 representing 39.5 percent and the least represented education level of the respondents in the tourism industry constitutes PHD holders with the 3 out of 286 representing 1
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Table 2. Gender (Source: Author, 2023).
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Figure 2. Gender (Source, Author, 2023).
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Table 3. Age distribution (Source: Author, 2023).
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Figure 3. Age distribution (Source: Author, 2023).
percent. This suggests that most people employed in the hotel industry are degree holders. Refer to Table 4 and Figure 4.
4.4. Inferential Statistics
Inferential statistics used the correlation of coefficient to help determine the existence of a relationship between the dependent variable and the independent variables. The overall results were analyzed using the Analysis of Variance (ANOVA) to examine the extent of the combined results to determine the extent to which the independent variables influence the adoption of e-marketing in the tourism sector.
4.5. The Relationship between Performance Expectancy and Actual Usage
There is a significant relationship between performance expectancy and actual usage since the P-Value from the SPSS output is below 0.01 which the level of significance as shown in Table 5. The coefficient of correlation of 0.261 suggests
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Table 4. Level of education (Source: Author, 2023).
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Table 5. Performance expectancy (Source: Author, 2023).
**Correlation is significant at the 0.01 level (2-tailed).
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Figure 4. Level of education (Source: Author, 2023).
that there a positive weak relationship between performance expectancy and actual usage.
4.6. The Relationship between Effort Expectancy and Actual Usage
There is no significant relationship between effort expectancy and actual usage since the P-Value from the SPSS output of 0.025 is above 0.01 which the level of significance. As shown in Table 6, the coefficient of correlation of 0.133 suggests that there a positive but very weak relationship between effort expectancy and actual usage.
4.7. The Relationship between Facilitating Conditions and Actual Usage
There is a significant relationship between Facilitating Conditions and Actual Usage since the P-Value from the SPSS output is below 0.01 which the level of significance. The coefficient of correlation of 0.428 suggests that there a positive moderate relationship between facilitating conditions and actual usage (see Table 7).
4.8. The Relationship between Social Influence and Actual Usage
There is a no significant relationship between social influence and Actual Usage since the P-Value from the SPSS output is higher than 0.01 which the level of significance. The coefficient of correlation of −0.063 suggests that there a weak relationship between social influence and actual usage. This suggests that social
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Table 6. Effort expectancy (Source: Author, 2023).
*Correlation is significant at the 0.01 level (2-tailed).
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Table 7. Facilitating conditions (Source: Author, 2023).
**Correlation is significant at the 0.01 level (2-tailed).
influence has no effect on the adoption of digital marketing by businesses in the tourism sector. Refer to Table 8.
4.9. The Relationship between Behavioural Intentions and Actual Usage
There is a no significant relationship between behavioural intentions and actual usage since the P-Value of 0.802 from the SPSS output is higher than 0.01 which the level of significance as showed in Table 9. The coefficient of correlation of 0.015 suggests that there a weak relationship between behavioural intentions and actual usage. This suggests that behavioural intentions have no effect on the adoption of digital marketing by businesses in the tourism sector.
Table 10 is a summary of the hypothesis results.
4.10. Regression Results
The regression output in Table 11 above from SPSS shows that all the five dependent variables which are PE, EE, FC, SI and BI are statistically significant with regard to actual usage in the tourism sector.
The summary regression model can be deduced as:
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Table 8. Social influence (Source: Author, 2023).
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Table 9. Behavioural intentions (Source: Author, 2023).
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Table 10. Summary of hypothesis results (Author, 2023).
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Table 11. Regression analysis output (Source: Author, 2023).
a. Dependent variable: actual usage.
The model suggests that all variables are positively related to the adoption e-marketing in the tourism sector in exception of social influence.
4.11. Analysis of Variance (ANOVA)
The results from SPSS on ANOVA indicates that the regression is statistically significant since the P-value of 0.000 < P-Value 0.05. This suggests that the model with actual usage as the dependent variable and PE, EE, FC, SI and BI significantly related. Please see Table 12.
a. Predictors: (Constant), Performance Expectancy, Social Influence, Behavioural Intentions, Effort Expectancy, Facilitating Conditions; b. Dependent Variable: Actual Usage.
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Table 13. Model summary (Source: Author, 2023).
4.12. Model Summary Using the Coefficient of Determination
As per Table 13, the overall coefficient of correlation of 0.722 in the table below from the SPSS results suggests that there is a strong positive relationship between actual usage and the independent variables which are PE, SI, BI, EE, and FC.
The R-Square (coefficient of determination) of 0.522 suggests that the adoption of digital marketing is influenced 52.2% by the independent variables PE, SI, BI, EE, and FC and 47.8% can be associated to other factors that we did not take into consideration.
5. Conclusion and Recommendations
5.1. Conclusion
1) The influence of performance expectancy on the adoption of e-marketing in the tourism sector
According to the results from the SPSS results, performance expectancy is significant to the adoption of e-marketing since the p-value is lower than the significance value of 0.01 and the coefficient of correlation of 0.261 is positive though not very strong since it is below 0.5. This suggests that, the perception on how the e-marketing systems will be positive among those involved in the tourism sector. The results are consistent with the findings in the study conducted by Mwinga and Phiri (2021) , with the assertion that, there was a positive relationship between performance expectancy and the adoption and use of e-PACRA services through the Government Services Bus (GSB) by users. The relationship between performance expectancy and adoption and use of e-PACRA services through the GSB by users was investigated using Pearson’s correlation coefficient.
2) The influence of effort expectancy on the adoption of e-marketing in the tourism sector
Effort expectancy and actual usage do not significantly correlate, as indicated by the P-Value of 0.025 from the SPSS output, which is above the significance level of 0.01. The 0.133 coefficient of correlation indicates a weak but positive relationship between expected and actual performance. The adoption process of e-marketing is unaffected by effort expectancy at the 1% significance level or 99% confidence level. In light of this, we can conclude that effort expectancy has no beneficial effect on the tourism industry’s adoption of e-marketing systems.
According to Mubanga and Phiri (2023) , in their study to investigate the factors affecting the marketeers to adopt the E-NAPSA services the results from their findings revealed that, effort expectance is not significant as based on the Pearson correlation of −0.096. From this it can be deduced that the marketeer’s effort expectant does not influence the usage of e-NAPSA services. This is line with the results obtained in this study. We can, therefore, deduce that effort expectancy has no significant influence and does not contribute to the adoption of technologies (ICT) systems.
3) The influence of facilitating conditions on the adoption of e-marketing in the tourism sector
Facilitating Conditions and Actual Usage have a significant relationship because the P-Value from the SPSS output is less than 0.01 the significance level. The correlation coefficient of 0.428 indicates a moderately positive relationship between the actual usage and the facilitating conditions. Again, the results are in line with the findings by Mwinga and Phiri (2021) , which suggested that, there is a positive relationship between Facilitating Conditions and the adoption and use of e-PACRA services through the Government Service Bus (GSB) by users.
4) The influence of social influence on the adoption of e-marketing in the tourism sector
According to the P-value from the SPSS output is greater than 0.01 (the level of significance), there is no significant relationship between social influence and Actual Usage. The −0.063 coefficient of correlation indicates a weak relationship between social influence and actual usage. This implies that social influence has no effect on the adoption of digital marketing by tourism-related businesses. These results still remain the same as study by Mubanga and Phiri (2023) , based on the UTAUT model, they asserted that, social influence is not significant as indicated in the table above with the Pearson correlation of 0.042. From this it can be deduced that the marketeers are not influenced by society’s perception on the usage of e-NAPSA service. In this regard we can conclude that the society does not affect people and businesses in accepting ICT systems.
There is, however, a divergent view from the study by Mwinga and Phiri (2021) , as the results obtained from their SPSS output suggested that, there is a positive relationship between Social Influence and the adoption and use of e-PACRA services through the Government Service Bus (GSB) by users. The results indicated a weak and positive relationship between Social Influence and the adoption and use of e-PACRA services through the Government Services Bus.
5) The influence of behavioral intention on the adoption of e-marketing in the tourism sector
Behavioural intentions and actual usage do not significantly correlate because the P-Value of 0.802 from the SPSS output is greater than the significance level of 0.01. There appears to be little correlation between behavioural intentions and actual usage, as indicated by the coefficient of correlation of 0.015. This implies that the adoption of digital marketing by companies in the tourism industry is unaffected by behavioural intentions.
According to Mubanga and Phiri (2023) , the results from their SPSS output indicated that the relationship between Behavioral Intentions and the use of e-NAPSA services by the marketeers in non-existent. A negative correlation between Behavioral Intentions and the marketers’ use of e-NAPSA services is indicated by the correlation coefficient of −0.181. This implies that marketers believe their behavior toward the use of e-NAPSA services does not support the adoption of the system and will not improve their own performance.
The overall coefficient of correlation of 0.722 from the SPSS results in this paper’s findings suggests that there is a strong positive relationship between actual usage and the independent variables which are PE, SI, BI, EE, and FC.
The overall R-Square (coefficient of determination) of 0.522 suggests that the adoption of digital marketing is influenced 52.2% by the independent variables PE, SI, BI, EE, and FC and 47.8% can be associated to other factors that we did not take into consideration.
5.2. Recommendations
SMEs in the tourism industry should consider investing in employee training for their marketing staff in e-marketing strategies.
SMEs in the tourism industry should consider investing in ICT equipment to enable smooth operations of e-marketing.
There is need for ongoing research and development efforts to stay abreast of emerging technologies and trends in e-marketing. This will help SMEs remain competitive in the dynamic digital landscape.
There is need for collaboration between the tourism industry and other sectors. For example, partnerships with information and communications technology companies can provide valuable expertise and resources to SMEs.
Acknowledgements
The author wishes to acknowledge all those who took part in the study. The author would also like to acknowledge the support of faculty at the Graduate School of Business.