Barriers to eMarketing Adoption
Primary data was collected using a quantitative research technique with the use of a structured questionnaire as the survey instrument. A total of 168 businesses were selected randomly and visited within the various municipal areas in the Oval Triangle. These businesses were visited between August and November 2008. Thirty-two small, medium and medium enterprises Seems refused to participate resulting in 123 usable questionnaires for the purposes of the analysis.
Factor analysis was used to examine the robustness of the factor structure using principal component analysis. Findings: A five-dimensional structure was established comprising a 16 item-scale. The major impediments towards the anticipation of e-marketing include technology incompatibility with target markets, lack of knowledge, stakeholder unawareness, technology disorientation and technology perception.
The reliability analysis, reflected coefficient values ranging from 0. 70 to 0. 88 indicating satisfactory internal consistency amongst variables within each dimension. Implications: By analyzing the barriers that inhibit the adoption of e-marketing strategies among Seems, marketers re presented with recommended strategies and implications on how to approach the challenges presented by Internet technological advancements.
Internet capacities of Seems may be strengthened through nurturing e-marketing awareness and providing adequate information tools through diverse Internet Marketing training programmer. Originality/Value: Seems can prove to be a
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INTRODUCTION Owing to the low growth rate of global economies, high unemployment and an unsatisfactorily high level of poverty in countries, the creation of the small and medium sized business sector plays a critical role in economic growth (Oeuvre & Greensward, 2007:269). The SEEM sector remains one of the most promising economic sectors contributing approximately 30% of the gross domestic product (GAP) in South Africa and over 80% in global economies Outlast, Bodywork & Thaliana, 2002:139).
Barriers to marketing Adoption By bigheartedness growth as well as for the development of the arts, human resources, manufacturing ND sport sectors. In line with the August report from the Parliamentary Monitoring Group (RASA, 2009), economies can benefit from the build-up of efficient Seems in order to facilitate the government”s plans to create about half a million Jobs for South Africans targeted originally for accomplishment by the end of the year, 2009. In this regard, uptake of Internet marketing strategies has the potential to contribute to the exponential growth of the SEEM sector.
However, despite its potential as a marketing tool, actual use of e-marketing has not met expectations (Elliot & Boohoos, 2007:16). Marketers are able to identify and satisfy customer needs and preferences through monitoring website visitations, e-mails, online surveys and chat rooms conducted on the Internet (Porter, 2001:68). Business 164 Acta Commercial 2010 enterprises can develop custom-made products and services that meet the precise needs of consumers and this in turn culminates in high returns for businesses (Zoo, 2005:56).
The adoption of e-marketing enables businesses to promote their products and services to the public through advertisements, banner advertisements, e-mails and mobile phone competitions (Chaffed & Smith, 2005:12). Businesses are also better able effectively to serve global markets and to distribute goods and services to customers globally through e-marketing adoption (Harris & Dennis, 2002:18). This article reviews the relevant literature on the importance of the SEEM sector, e- marketing and the barriers to e-marketing adoption.
In addition, the generic problems regarding the adoption of e-marketing are highlighted. THE SEEM SECTOR AND ITS IMPORTANCE Small enterprises comprise between five and 100 formally employed persons. Employees are employed on a full-time basis. According to the Department of Trade ND Industry, a small business is owner managed; registered with local authorities and business activities are conducted from fixed premises (RASA, 2005:5). Medium enterprises constitute between 100 and 200 formal employees (Crone et al. 2004:5). Such businesses have vast potential of growing into large businesses, should capital injections be obtained. Often they are characterized by a decentralization of power to a new management layer (Fallen, 2000:25-27) between the SEEM operator and employees. Seems are considered the “lifeblood of modern economies” (Raw, Meets & Monger, 003:13), creating far more Jobs than those created by large businesses, contributing 37 percent to employment in South Africa (Counties, 2002:712; Barry & Miller, 2002:316).
Seems provide a variety of goods and services for customers to choose from, some of which might otherwise not be provided by large businesses Jackson, the arts, human resources, manufacturing and sport sectors (Crone et al. , 2004:6). Studies conducted by Moodily (2002:37) from a development perspective reported that Seems are important because of their potential for Job creation and distribution f wealth which results in a multiplier effect on the socio-economic activities of a country which in turn creates a multiplier effect on the socio-economic developmental activities of a country (Wrack & Gad, 2007).
Seems are flexible and can also act as subcontractors to most large enterprises in the economy, ultimately leading to equitable distribution of income (Lloyd 2002:8). The Department of Trade and Industry report (RASA:1995) indicates that Seems provide personalized services and also make a positive contribution to wealth creation in the South African economy. They are breeding incubators for entrepreneurial talent and testing ground for new products (Chasten, 2000:74). They are agents of change, widely facilitating innovation and competition within various national economies (Barry & Miller, 2002:317).
This sector tends to differ from large enterprises in that they stimulate competition, bring about a diversity of products and services, have less formality in their internal and external systems and possess hands-on managerial style, which facilitates faster decision-making times (Kendall, Tuning, Chug, Dennis & -ran, 2001:225). Despite the concerted efforts by the South African government to eliminate potential arises to technology adoption through the liberation’s in the telecommunications sector, this growth has not significantly filtered down to the SEEM sector (Upon & Eastman, 1999:9). 65 The generic barriers to e-marketing adoption are discussed in the next section. E- MARKETING AND GENERIC BARRIERS TO ADOPTION The E-Marketing Association defines e-marketing as “the use of electronic data and applications for planning and executing the conception, distribution, promotion and pricing of ideas, goods and services to create exchanges that satisfy individual and organizational objectives” (Gharry, 2007:4). Burgess and Bottom (2007:397) define e- marketing as a “business”s efforts to inform, converse, promote and sell products and services over the Internet”.
In knowledge economies, high competition among profit-making businesses is of paramount importance in order to attract customers. Decision-making times and sales cycles are becoming shorter (Risen, 2004:48). There is also a need to keep pace with contemporary business requirements in ensuring that marketing”s explains why it is that Seems should integrate Internet-driven marketing principles into their mainstream business practices.
A review of literature reveals that the adoption of e-marketing varies by industry type (Upon & Eastman, 1999:14) with the public, education and charity organizations being the lowest adopters of Internet technology (Maguire, Koch & Magmas, 2007:39). Global adoption of e-marketing by Seems has been slowest in the agricultural sector (Sparker & Thomas, 2001:332). This refutes earlier research findings of Too and Tan (1998:342) who found that no significant relationship exists between industry sector and adoption of marketing.
Businesses in the services sector, primarily the consultancy and professional services, have reported a robust interest in adopting Internet technologies for racketing purposes as their type of business integration necessitates the use of computer technologies as a core activity (Cowbell, Broodier, Brooked & Palmer, 2004:15). Websites in the airline, hospitality, software and electronics industries indicate that the adoption of the Internet has been extensive in these sectors (Kalmia & McIntyre, 2002:468).
Seems specializing in manufacturing products are less likely to adopt Internet technologies as compared with knowledge intensive service organizations such as consultancies (Martin & Mayday, 2001 :403; Shadow’s, Midland & Dongle, 2001:90). A majority of manufacturing Seems are still in the lower stages of e- business adoption because these firms perceive very low levels of benefit from e- business (Ramsey & Batons 2006:317). Distinctiveness’s industries have reaped the rewards of Internet technology, which are yet to be experienced by traditional marketers (Chain & Eastman, 2000:72).
These high-tech firms constitute early adopters of Internet technologies (Fills, Johansson & Wagner, 2004:182). This is contrary to studies undertaken by Mayday and Addis (2003:323) who assert that there is no connection between Internet adoption and technological content of a business environment. If a firm has inadequate security procedures or an unrecognized brand name this could Jeopardize customers” confidence in the usefulness of the Internet as a trading platform (Durban, Durbin & Dilled, 2003:101).
This is because any online research and purchasing decisions are made solely based on trust (Wagoner 2004:39). Inadequate security measures, expertise and financial means to guard against unauthorized access to confidential information by employees and from outsiders and hackers pose a hindrance to Internet adoption (Khan 2007:24-25; Wallach, Braver & Lundeberg, 2000:566). E-marketing leads to standardization of products and prices as differences among competitors” products are reduced (Porter, 2001:73).
Large firms are also able to encroach onto niche markets, which were traditionally serviced by Seems because e- marketing significantly reduces transaction costs Factor, Chapel & Feint, 166 2002:124). These limitations on the part of Seems have contributed to the significantly low levels of marketing adoption. However, the turning point comes when marketers out-compete each other based on information and service quality (Hamlin, 1997:306; Elliot & Sewer, 2006:43).
Drew (2003:86) makes the point that the costs of infrastructure, access and adoption of e-marketing have declined to levels where these no longer present a barrier. Adoption is often impeded by information barriers such as uncertainties with respect to the performance of the Internet or the future development of these technologies, within the SEEM context (Holstein, 2004:321). Some Seems occupy small niches in the local markets where word of mouth” is a guarantee for quality such that the adoption of Internet for marketing purposes may be viewed as an inhibitor for their business communications (Taylor & Murphy, 004:285).
PROBLEM STATEMENT The Internet is the fastest growing technology in the world, taking approximately seven years to reach a 25% market share from its conception, as opposed to the telephone that took 35 years, and the television, which took 26 years (Sings, 2002:3). There is little evidence of long-term strategic development of e-marketing technologies within Seems (Fills et al. , 2004:180). Only 17 percent of the Seems in South Africa believe that electronic business transactions are critical to their operations (Cayman, 2003:2). From these current users, considerable variations still exist in heir adoption of e-marketing.
Some owners of very small businesses may adopt e- marketing as a means of defending their autonomy in business and thus adopt the technologies in a casual and ad hoc manner (Gilmore, Gallagher & Henry, 2007:234). This is the current scenario despite ample evidence to suggest that e-marketing can facilitate improved business practices particularly within the small and medium sized business fraternity (Whitely, 2000:217). Several barriers have been cited in the literature as contributing largely to the non-adoption of e-marketing strategies.
Some mall firms may be resistant to embrace online technologies due to perceived risks that include privacy and security issues (Liebermann & Stashes, 2002:291). A research conducted in Asia highlighted that globally, Seems are not prepared to adopt e-marketing as a serious business concept owing to the limited acceptance of online selling by consumers (Lane et al. , 2004:10). Findings of Sings (2002:6) further alluded to the fact that customers are reluctant to shop online due to insufficient knowledge and limited trust in the use of credit cards as well as the issue of delivery of online purchases.
However, adoption is often impeded by information barriers such as uncertainties with respect to the performance of the Internet or to the future development of these technologies globally (Holstein, 2004:321). In a study carried out by Johnston and Wright (2004:228) on the barriers affecting the implementation of Internet systems and procedures in different countries, it was revealed that the following were the rankings in order from the most prohibitive barrier to the least of barriers: High cost of installing infrastructure; high price of technology, large Uncertain return on investment (ROI);
Limited worker expertise caused by a general shortage of highly skilled workers and insufficient training; Lack of management vision, support and enthusiasm in the adoption of Internet technology, Inability to outsource IT expertise; and Bad experiences in the past. 167 However, these rankings are not consistent across countries. Further research has shown various related issues to be possible inhibitors of e-marketing adoption. Lack of training, capital and understanding of the potential benefits brought about by Internet technology have been cited as key barriers to the adoption of marketing by
Seems (Micro & Adagio, 2005:70). Other studies consider the lack of knowledge as a factor made manifest in a lack of awareness; advice and support or having a staff compliment without the necessary IT skills (Standstill & Grant, 2003:23; John & Hushing, 2006:993). SEEM owners pursuing unclear business strategies often contribute to their businesses losing sight of the value of adopting Internet technology (Micro & Adagio, 2005:70).
Other researchers have highlighted the absence of a national and international regulatory framework related to privacy and security as a major concern or Seems not adopting the Internet (Lewis & Cockerel, 2002:199). PURPOSE OF THE STUDY The paper seeks to advance the findings advocated in previous studies on e- marketing barriers that exist among Seems by establishing barriers to e-marketing using a factor analytical approach. METHODOLOGY To obtain an objective perspective, a literature study was conducted on barriers inhibiting the systematic adoption of e-marketing as well as an empirical investigation.
Primary data was collected using a quantitative research technique with the use of a structured questionnaire as the survey instrument. The rationale for selecting a quantitative study was that it is cheaper, flexible and allows for replication of the research procedure thus enhancing validity of research findings. Quantitative studies possess the rigor and coherence that is necessary for addressing the issues and problems (Malaria, 2004:137) that underpin the anticipation of e-marketing by Seems.
Population and the sample The historical evidence approach was used to determine the sample size for this research (Sigmund, 1999:320). A sample size of 1 50 was set and therefore deemed appropriate and feasible for this particular study. This figure is also consistent with sample guidelines from past similar surveys conducted by Uphold and Sewer (2006:5); constituted small and medium sized businesses in the Oval Triangle. The target population was restricted to Managers, SEEM owners, IT specialists, or Heads of Marketing Departments.
An appropriate sampling frame was assembled from various lists that included a register from the SautĂ©ing Enterprise Propeller (KEEP), the Oval Triangle business directory as well as SEEM databases from the relevant municipalities in the region. Seems were randomly selected from the population so ACH population unit had an equally non-zero chance of being selected thus allowing statistical inferences to be made (Bradley, 2007:172). Data collection Three fieldworkers to conduct the interviews were selected by the researchers based on their ability to understand the concept of e-marketing.
These students were screened and selected from a Marketing Research 4 class from a university. They were trained in various aspects of questionnaire fieldwork administration. Pre-testing was conducted on five academics in marketing and IT fields in order to ensure that the questionnaire met expectations in providing accurate information ND to assess whether or not respondents understood the questions correctly. In addition, a pilot study was conducted with 20 Seems (10 small and 10 medium enterprises). This technique was used as an indispensable aid for developing the final questionnaire. 68 The views of SEEM operators and other researchers were taken into account prior to conducting the main survey. Suggestions were taken into account resulting in the compilation of the final measuring instrument. A survey method was used as it a satisfactory means of assessing information about a population with an ease of administration (Sigmund, 2000:220). A total of 168 businesses within the various municipal areas in the Oval Triangle were selected randomly and visited between August and November 2008.
Thirty-two Seems refused to participate resulting in 123 usable questionnaires for the purposes of the analysis. RESULTS The results are described taking into account the composition of the sample in terms of small and medium enterprises, demographic analysis of the data and descriptive statistics explained by the means of the variables relating to the barriers to e- marketing adoption. Thereafter, the factor analytical procedure and extraction of factors is described.
The sample composition A total of 115 businesses (93%) were classified within the small sector whereas only eight firms (7%) qualified for classification as medium enterprises based on number Table 1 Sample composition Enterprise size Small Medium TOTAL Number of employees Fewer than 100 Greater than 100 but less than 200 Annual turnover (rand) Less than 8 Million Greater than 8 Million Count (n) 115 8 123 93 7 Demographic data of the respondents was also collected including gender, age and education levels. Of the 123 businesses that were included in the survey, 77 respondents (63%) were males and 46 respondents were females (37%).
The largest category of respondents was between 40 to 49 years which comprised 39 percent of the sample (n=48). This was followed by the 30 to 39 years age group which represented 34% of the sample (n=39). 15 percent of the respondents were under 30 years of age, and 12 percent fell in the 50 years and older age category. Seven out of ten standard industry classification (SIC) sectors were represented in the sample. The industry representation of the sample is reported in Figure 1. The majority of respondents comprised the community, social and personal services sectors which constituted 32 respondents (26%).
This sector comprises mostly hairdressing, medical care as well as recreational services. The wholesale, retail, clothing, hotel and restaurant business sector comprised 21 respondents (17%) followed by the manufacturing and financial services sectors each constituting 18 respondents (15%). The transport, storage and communication sector comprised 14 respondents (11%) of the sample. 169 Figure 1: Industry representation of sample 32% 35% 25% 5% 21% 18% Agriculture, hunting, forestry & fishing Manufacturing Construction 14% Wholesale and Transport, storage & retail, clothing, communication hotels & restaurants
Financial intermediaries personal & other activities Descriptive analysis on barriers to e-marketing adoption With the exploratory nature of the study, the data was initially analyses using descriptive statistics. Table 2 reports on e-marketing adoption barriers. On the perceptions of the barriers inherent in the implementation of various e-marketing strategies, respondents were requested to list the top five inhibitive factors of marketing adoption. The most critical challenge facing Seems was identified as the unavailability of resources to implement the systems.
Of the 123 Seems which were revered, 23 percent (28 respondents) strongly agreed and 62 percent (77 respondents) agreed to the statement that they find e-marketing too expensive to implement. Furthermore, 29 percent (35 respondents) somewhat agreed with the statement that e-marketing is a security threat to their business. Lack of knowledge,training, advice and support have also been highlighted as being major factors in the adoption process. Approximately 54 percent (67 respondents) of the Seems cited that they do not know much about e-marketing.
In the same vein, 48 percent (58 respondents) somewhat agreed that their organizations do not possess he necessary staff skills required for the complete implementation of e-marketing. 170 Table 2: Descriptive analysis – barriers to e-marketing adoption Factor analysis In addition to obtaining the perceptions on the barriers to adoption of e-marketing a principal components factor analysis was conducted on the twenty-item scale to develop the set of factors that can be classified as barriers to adoption of e-marketing among Seems. Prior to factor analysis the appropriateness of absorbability on the data set was established.
Examination of the correlation matrix (strength of linear association among variables) revealed that a substantial number (75%) of the variables were >0. 30 which according to Vagina (1994:14) indicates absorbability. The Kaiser-Meyer-Oilskin (KM) and the Bartlett”s tests were also applied in order to further determine the appropriateness of the data set for factor analysis. The approximate chi-square was 827. 888 with 120 degrees of freedom and significant at Pl) leading to the final extraction of five factors. Compilation of the screen plot further posited that the screen began to level off after five factors.