Towards a deeper understanding on the influence of marketing
performance measurement and strategic orientation on
performance (financial and non-financial) of Dutch SMEs
performing Search Engine Advertising.
Simon Haijma
Towards a deeper understanding on the influence of marketing
performance measurement and strategic orientation on
performance (financial and non-financial) of Dutch SMEs
performing Search Engine Advertising.
Master thesis
Author: Simon Haijma
(1750070)
Rijksuniversiteit Groningen
Faculty of Economics and Business
Small Business and Entrepreneurship
FIrst supervisor: H. Zhou
Second supervisor: C.H.M. Lutz
MANAGEMENT SUMMARY
TABLE OF CONTENT
§1 INTRODUCTION...6
§1.1 Introduction...6
§1.2 Aim of the research...7
§1.3 Motivations for research...7
§1.4 Type of research...10
§1.5 Structure of thesis...10
§2 THEORETICAL BACKGROUND...11
§2.1 Literature review on key concepts...11
§2.1.1 Small and Medium sized Entreprises (SMEs)...11
§2.1.2 Internet marketing (IM)...12
§2.1.3 Outcomes...15
§2.1.4 Marketing performance measurement (MPM)...16
§2.1.5 Internet marketing metrics...17
§2.1.6 Firm strategic orientation: entrepreneurial orientation (EO)...20
§2.1.7 Firm strategic orientation: small business orientation (SBO)...22
§2.2 Hypothesis formulation...23
§2.2.1 Marketing performance measurement relationship with outcomes...23
§2.2.2 Internet marketing metrics relationship with outcomes...23
§2.2.3 Entrepreneurial orientation relationship with outcomes...24
§2.2.4 Small business orientation relationship with outcomes...25
§2.3 Research model...26
§3 METHODOLOGY...27
§3.1 Sample and data collection...27
§4 RESULTS...36
§4.1 Descriptives...36
§4.2 Correlation between constructs...37
§4.3 Regression analysis...39
§4.3.1 Univariate regression...39
§4.3.2 Multivariate regression...42
§5 CONCLUSION...50
§5.1 Discussion...50
§5.2 Practical implications...53
§5.3 Limitations and future research...55
REFERENCES
56
Appendix A: Online Questionnaire (distributed March, April 2012)
63
Appendix B: Missing value procedure
68
Appendix C: Sample distribution
69
Appendix D: Overview measurement items
70
Appendix E: Dependent variables overview (incl. factor and Cronbach’s ) 71
Appendix F: Type of internet marketing metric used
72
§1. INTRODUCTION
§1.1 Introduction
In the last decades, the role of internet in today’s business world has grown enormously. When focussing on SMEs (small and medium sized enterprises) , internet based technologies provide small firms with “the opportunity to overcome limitations of size and to compete more effectively and/or in larger markets with bigger sized establishments (Dholakia; 2004). Based on Johnston et al. (2007), the implementation of internet based solutions results into financial gains for SMEs. According to Williams (1999), “internet technologies increase the ability of small firms to compete with other companies both locally and nationally” using the internet in promotion (“internet marketing”).
In the context of internet marketing, the topic of search engine marketing (SEM) is an interesting topic to focus on. SEM can be seen as a relatively new form of marketing that uses internet search engines to draw attention from potential customers. Based on Xing & Lin (2006) there “has been a tremendous surge of interest in internet search engines”. Search engines has gained enormous popularity in the last decade (Brooks; 2004). Internet search engines have “become popular both as information-seeking instruments and as online advertising media” (Xing & Lin; 2006). In the last years, Google has become a billion-dollar giant in the online advertising market thanks to SEM. An increasing number of people and companies are using the internet to research and seek information on their purchasing decisions (Green; 2003), and according to Sullivan (2006, from Murphy & Kielgast; 2010), around “85 percent of all purchasing activities on the internet begin with a ‘search’, with obvious repercussions on online purchase behaviour”.
§1.2 Aim of the research
This study aims to provide a better understanding on how outcomes are influenced in small businesses conducting SEA. An emphasis is put on the role of strategic orientation (Runyan; 2008) and marketing performance measurement (O’Sullivan & Abela; 2007) of small firms in influencing outcomes financially and non-financially. This research wants to provide understanding to what extent internet marketing metrics influence a small firm financial performance and an owners satisfaction with SEA. The following research questions and subquestions are presented (further explained in the following paragraph “motivations”),
Research questions:
R1: How is financial firm performance influenced for SMEs conducting SEA? R2: How is owner satisfaction influenced for SMEs conducting SEA?
Subquestions:
S1: How does the firm’s marketing performance measurement influence firm performance at small businesses?
S2: How does SEA metric measurement leads to the best firm performance?
S3: How does the strategic orientation of the small business conducting SEA influence firm performance?
§1.3 Motivations for research
Practical motivations
to focus on SMEs, because SMEs are a major part of the industrial economies (Eikebrokk et al.; 2007). Estimations in the Netherlands indicate that around 99% of total businesses can be characterized as SMEs. Hence, they account for 58% of total turnover and 60% of total employment jobs1.
Although SEM can lead to an increase in profits (Yang & Ghose; 2010) and small businesses seem to recognize the need to use the internet as a marketing tool, in many cases expectations seem to fall behind with actual results. In their research on the impact of internet on small business marketing activities in Northern Ireland, Gilmore et al. (2007) state that most SMEs do not use internet marketing to “its full scope and potential”. First of all, SMEs struggle to be visible on the web (Murphy & Kielgast; 2010). In their research on Australian e-commerce adoption and evaluation at SMEs, Lin et al. (2007) state that the evaluation of e-commerce decisions falls short. Evaluation is sparse, and a “proper assessment of business needs before adopting IT investment in e-commerce” is lacking. In addition, research in the SEM context also finds that the measurement of SEM remains a challenge, with many firms being unsure if they measure it effectively (Barry et al.; 2009).
Above discussion raises the need for a deeper understanding of the evaluation of SEM at SMEs, which this research aims to provide. Now that the practical motivations for a research on SEM at SMEs are clarified, it is interesting to look deeper at the academic motivations for conducting this research.
Academic motivations
This research will add to prior academic work in two ways. First, the earlier mentioned (see “practical motivations”) evaluation and measurement of SEM at SMEs. Gilmore et al. (2007) state that SMEs need to critically evaluate the value (goal) of internet presence, as an investment to generate sales through internet marketing, or merely as having a website to gain (brand) exposure. Their internet marketing investment should be based on strategic goals (Eid and Trueman; 2006). According to Lace (2004) future research could shed light in finding out how marketeers are evaluating the effectiveness of their internet advertising and web-site performance and how successful they perceive to be in doing so. As far as the author is aware off, until now, no academic work has filled this literature gap for SEA business efforts, adding to the academic relevance of this research.
When focussing on this evaluation of marketing performance, research on high-tech companies already showed that the ability to measure marketing performance (MPM) had “a significant impact on firm performance, profitability, and marketing’s stature within the firm” (O’Sullivan & Abela;
2007). This could make the measurement of SEM investments essential for firms, including SMEs, raising the relevance of MPM in this research. In addition, small firms are often constraint in (financial) resources, making effective measurement of investments even more critical. Therefore, subquestion 1 is formulated to investigate the effect of MPM on firm performance.
For the measurement of SEM investments, internet marketing metrics are used (such as conversion rate, number of clicks and rank). Kennedy & Kennedy (2009) state that sales conversion is a more accurate metric of return of investment (ROI), because traffic (e.g. more clicks) does not necessary result in an increase in sales. Dou et al. (2010) find that attention to search engine effectiveness “largely centers on the number of clicks generated (Kitts and LeBlanc; 2004)”. This despite the fact that extensive evidence shows that some advertisers are not focussing on number of clicks but e.g. branding impact (Dou et al.; 2010).
Above academic work of Kennedy & Kennedy (2009) and Dou et al. (2010) raise the question: which internet marketing metrics are really important for SMEs? Therefore, this research will aim to clear uncertainty in prior academic work by investigating which internet marketing metrics influence firm performance (such as ROI) at SMEs, resulting in subquestion 2.
Combining the three subquestions results in two main research questions. First, how is financial performance influenced for SMEs conducting SEA? And second, how is owner satisfaction influenced for SMES conducting SEA?
Concluding above discussion, this research adds depth to existing literature work by involving the influence of individual internet metrics on performance outcomes, thereby sheding light on how a small firms’ SEA investment is measured and how effective they are in doing so. In addition, it fills existing literature gaps by applying the concepts of MPM and strategic orientation onto the SEA market.
§1.4 Type of Research:
This study will be quantitative, consisting of a literature research and an empirical research. To collect data, surveys will be distributed accross firms conducting SEA on the search engine Google. Firms active in the sectors “Clothing”, “Tourism & Recreation”, and “Business, Consumer and Online Services” will be targetted to make discrimination between sectors possible. The input collected from the questionnaires will be used to test hypotheses, ultimately resulting in a satisfactory answer on the formulated research questions.
Other data collection methods in this thesis will involve:
- Literature on internet marketing measurement (e.g. cost per click) - Literature on SME strategy and internet adoption
- Literature on SEM (see also references)
§1.5 Structure of the thesis
§2 THEORETICAL BACKGROUND
First, all the relevant concepts of this study are introduced in an extensive literature review. Hypotheses are formulated based on this literature review. Second, a conceptual model is derived and explained using relevant literature.
§2.1 Literature review on key concepts
§2.1.1 Small and Medium sized Enterprises (SMEs)
In the introduction the importance of SMEs in business activity was stressed; almost 99% of total businesses in the Netherlands can be characterized as SMEs, being considered as an important force in creating Dutch employment jobs. Therefore it is necessary to get a clear definition on SMEs. Several definitions exist on SMEs, that all vary slighty in characterizing SMEs in terms of size, revenue and employees employed (Carter & Jones-Evans; 2006). In this case, quantitative criteria will be used to define SMEs. To be more specific, the amount of people working in the business will be used as a criteria to select SMEs. Other criteria, such as the total amount of turnover, often involve financial data, which is not easily obtainable, because company owners may be reluctant to provide such sensitive data. This research resides in the Netherlands and therefore a definition will be used that shows a fit with the Dutch business culture. The Netherlands as a country is part of the European Union, and therefore the official EU definition of SMEs will be used in this research (Wiklund; 2005). According to this definition, companies can be distinguished as micro, small and medium companies. Micro companies account for 44% of business activity in the Netherlands, and can be defined as businesses with less than 10 employees2. Small companies have 10 or more employees, but less than 50 employees working in the company, and medium sized enterprises have 50 or more, but less than 250 employees active in the company. In addition, in this research, the self-employed company owners are taken into account. This last group accounts for 46% of total business activity in the Netherlands, and therefore can be considered as a substantial group. Because some authors cited in this thesis use the term “small businesses”, the term ”SMEs” will be interchangeably used with the term “small businesses”, meaning the same.
§2.1.2 Internet marketing (IM)
Like mentioned in the previous section, SMEs play a major role in total business activity in the Netherlands. Therefore, SMEs have also experienced the influence of the internet throughout the last decade. Like mentioned in the introduction, internet had its impact on day-to-day business. For SMEs, internet enables small companies to compete with bigger, established companies. Dholakia (2004) distinguishes the use of the internet by SMEs in terms of ownership of a website (adoption), and the use of the internet for selling purposes (routinization). However, since 2004 things have changed, internet adoption is widespread, and has become even more embedded in day-to-day business of small firms. According to Gilmore et al. (2007), “the use of internet for E-Commerce (technologically mediated exchanges) has grown rapidly in relation to the increase in commercial web sites”.
Internet enables companies to request and provide information, and to place orders. It also enables companies to deliver products or to perform services (Ching and Ellis; 2004). In addition, the internet provided SMEs with the ability to enter foreign markets by allowing them to “communicate globally as efficiently as any large business (Gilmore et al.; 2007). In essence, according to Williams (1999), internet technologies increase the ability of small firms to compete with other (larger) companies both locally and nationally (promotional tool).
According to Gilmore et al. (2007), internet marketing (or IM) can be seen as a logical extension of the traditional marketing practices in creating, communicating and delivering value to customers, now with the help of the internet. The definition used in this thesis is derived from Gilmore et al. (2007):
Search engine marketing (SEM)
In order to achieve a company’s set marketing objectives with the help of the internet, the role of search engine marketing comes into place. The rise of search engines the last years has resulted in companies performing online advertising activities to target and acquire new customers through this “new” search engine media. SEM consists of two types of marketing, search engine optimization (SEO) and search engine advertising (SEA) or paid placement (Dou et al; 2010, Xing & Lin; 2006; Sen; 2005). SEO focusses on optimizing websites in such a way that their ranking on the organic (or natural) search engine result pages (SERPs) is improved. In this research, an emphasis is put on SEA, which mainly focusses on the usage of relevant keywords in search results of search engines. There are a couple of reasons for SMEs to choose for SEA over SEO. First of all, with SEA, a position on SERPs could be guarenteed and implemented instantly (Laffey; 2007), where SEO is more difficult to achieve. Organic results on search engines (e.g. Google) are based on relevance. Optimizing a website to the ever changing (secret) algorhytm of Google is a difficult process. Without good SEO it is for small companies difficult, if not impossible, to land on top of the natural SERPs, because larger companies (with higher budgets and SEO expertise) are often seen as more relevant and will be found on the top places. However, when effectively using SEO expertise, small companies in some cases can land above “bigger” brands. Big companies may have failed to develop a “coherent” SEM strategy, which leads to a “lack of competitive vigilance” (Dou et al; 2010). However, like mentioned before, in most cases it is hard to get high landing results with SEO as a small company. In these cases, high landing results with SEA are easier to achieve (Laffey; 2007). Hence, according to Ghose & Yang (2008), the mean conversion rate (i.e. sales) of paid searches is higher than for natural searches (or “queries”). In addition, the mean order value of consumers and, ultimately, profit for paid search advertisements was “higher than that for natural listings” (Ghose & Yang; 2008).
Search engine advertising (SEA)
Based on the discussion above, SEA is an appropriate online marketing choice for small businesses. In the paid search (SEA) model (Laffey; 2007), sponsored listing position (or ad rank) is determined on the amount an advertiser wants to pay for the keyword or phrase (cost per click) and on the quality score. The ad “must exceed a certain quality threshold, which helps ensure that only the highest quality ads appear in top spots” 3. The quality score is determined by Clickthrough Rate (CTR, explained in section “Internet marketing metrics”) and landing page quality, which in turn is a
weighted average of relevancy, transparency and navigability of a given weblink (Ghose & Yang; 2009). Therefore it is also very important to give relevant information on the landing page, resulting in higher user experience after a click-through to the site (Ghose & Yang; 2009). Like mentioned before, advertisers can choose to bid on relevant keywords or phrases. In the context of this research, it is expected that small businesses use generic keywords or phrases instead of branded keywords and phrases. Most small businesses conducting SEA do not have an established brand, and SEA is used to get consumer attention on the internet in the first place (Rutz & Bucklin; 2008). Choosing an appropriate sample of small businesses conducting SEA will therefore be based on generic search queries.
Advertisers can bid for positions (“rank”) in ongoing auctions. When a user searches for a specific keyword, the order of the sponsored results is determined by the current bids in the auction. Payment is made by advertisers each time a user searches for a term and then clicks on their link. The cost of a paid placement is measured by CPC (cost per click) times the number of clicks the advertisement receives (Xing & Lin; 2006). According to Green (2003), these search adverts work because “increasing numbers of people and companies are using the internet to research and inform their purchasing decisions”. Listings only appear when a search engine user generates a keyword query, therefore advertisers “can reach a more targeted audience” (Ghose & Yang; 2009). An indication of the actual scope of this market; only in the United States in the first half of 2011, the total revenues on SEM increased to $7.3 billion dollars4. Google AdWords takes care of placing advertisements in the paid listing area of the Google search engine, shown as “Sponsored Links” (Pan et al.; 2010). This research uses Google for finding Dutch SMEs conducting SEA. Google is the most appropriate search engine for this research, because it accounts for 93% of Dutch online search behaviour.5 Therefore, most SMEs conducting SEA in the Netherlands will use Google’s advertising program called Google Adwords for internet advertising. Google AdWords offers the ability to track the number of clicks, cost per click (CPC), number of conversions (e.g. sales) and the costs per conversion. However, for the actual measurement of conversion a code snippet is needed6 (further explained in section “Internet marketing metrics”).
4
Search Engine Watch - searchenginewatch.com/article/2112920/Search-Advertising-Revenues-Hit-7.3-Billion-in-First-Half-of-2011-Report
5 Statcounter market share search engines in the Netherlands -
www.edwords.nl/2011/07/08/marktaandeel-zoekmachines-nederland-2011/
§2.1.3 Outcomes
Several key concepts described in this thesis have an influence on firm outcomes. In order to measure the concepts in this research, it is essential to define the concept “outcomes”. To clearify this concept relevant literature will be used an applied to the context under study, small firms conducting SEA.
In their research on MPM, O’Sullivan & Abela (2007) distinguishes two different kinds of outcomes for businesses. First, the CEO satisfaction with the marketing investment. Based on Srivastana, Shervani and Fahey (1998) he argues that firm marketers struggle to communicate the impact of marketing on firm performance. In addition, Lehmann (2004), and Webster, Malter, and Ganesan (2005) observe that marketing “has the greatest influence and stature in firms in which there are clear measures of marketing’s contribution”. O’Sullivan & Abela (2007) find that a companies MPM ability positively influences CEO satisfaction, implying that a company’s ability to measure marketing performance, and a company’s ability to generate metrics to measure marketing performance, positively influence CEO satisfaction with marketing, in this case SEA. In this research, the firms under study are small businesses. Therefore, the constructs mentioned by O’Sullivan & Abela (2007) need to be adapted to the context under study. In this research, the term “CEO” is therefore replaced by “Owner”. The term “CEO” is more appropriate for larger companies, where the term “Owner” is more appropriate in a small business setting. The term “Entrepreneur” was also considered, but may not show a fit with some small business owners, implying that not all small business owners may characterize themselves as “Entrepreneurs”.
if most small business have a clear understanding of their performance relative to all other competition. It is important to consider self-perception, subjective data on the construct of financial performance, because small businesses may be reluctant to provide financial data to third parties (Runyan; 2008, Megicks; 2001). In addition, using objective financial data from different sectors may be misleading and cause bias (Megicks; 2001). According to Runyan (2008), these “subjective assessments of performance can accurately reflect objective internal measures such as revenue and profit”. Therefore, subjective assessments of financial performance may be used in this research. This will be explained further in the methodology section of this thesis.
§2.1.4 Marketing performance measurement (MPM)
Companies that are choosing for SEA as a marketing tool have to be aware that SEA always involves a certain, sometimes substantial, investment. In order to get high rankings in the SERPs, a certain financial investment has to be made. This, in combination with the fact that small firms are often constraint in (financial) resources, raises the need for a proper assessment of the marketing investment, where benefits exceed costs. Therefore, in order to demonstrate the value of SEA for the firm, it is needed to measure the marketing investment. This discussion is fueled by work on marketing accountability mentioned in several papers such as Stewart (2008). Stewart (2008) lists numerous papers (Young et al; 2006, Srivastava & Reibstein; 2005, Rust et al; 2004) in the last years that call for marketing “to become more accountable and to demonstrate what marketing contributes to the firm”. In addition, based on an interview with a CFO of a Fortune 100 company, Stewart (2008) finds that “if the marketing discipline cannot demonstrate its value, it will continue to be merely a set of tactical activities in which costs must be controlled”. Hence, marketing is not likely to “take place at the strategic planning table without hard evidence of what it contributes to the firm’s bottom line”. Thus, an excellent option to raise marketing accountability, and to gain marketing support in organisations, is to measure its financial outcomes. O’Sullivan & Abela (2007) base their research on this stream of literature, and shows that marketing performance measurement (MPM) can help in measuring the value of the marketing investment.
measurement items to measure a company’s ability to generate marketing performance metrics, involving a financial item, a non-financial item, and two items that measure a firm’s ability to generate benchmark information. According to O’Sullivan & Abela (2007) the concept of MPM is based on the assumption that a firm’ measurement efforts are beneficial for a firm’s performance. Thereby, like mentioned before, it offers the possibility to measure whether a marketings investment can be justified, ultimately raising marketing accountability.
§2.1.5 Internet marketing metrics
Like mentioned in the previous section, in a companies choice of metrics, firms should use both financial and non-financial metrics (Clark; 1999, Rust et al.; 2004). The MPM focusses on the ability to measure a company’s performance using these metrics. In the specific context under study, the SEA market, the distinguishing between financial and non-financial metrics is also usable. Examples of more financial internet marketing metrics are sales (conversions) generated, and profitability earned in a given period, where non-financial internet marketing metrics focus on web traffic, number of unique visitors etc. The two types of metrics seem to complement eachother (Truman et al.; 2000). For example, the more unique visitors, the more likely that sales are being generated.
It would be interesting and add depth to existing literature (O’Sullivan & Abela; 2007) to dig deeper in how performance is measured and to test which marketing metrics are used. Some authors (e.g. Rutz & Bucklin; 2007) use the term “campaign metrics” for characterizing these metrics. In their research on the spillover effects in SEA, Rutz & Bucklin (2008) mention stand-alone metrics of performance (click-through or conversion rate) or financial return (cost per click or cost per conversion). When focussing on these metrics, two Google Adwords campaign goals can be distinguished7. Businesses using Google Adwords can focus on conversion campaigns or click campaigns. Businesses that focus on conversion campaign in general want to generate leads, applications or enrollments, online sales, repeat sales, or traffic to offline stores (“conversions”). For reader clarification, the different types of internet marketing metrics will be described.
7
Conversion metrics
For small businesses pursuing a conversion goal, it is important to measure the cost per conversion. Google Adwords offers small businesses the ability to track the conversion by implementing a code snippet on the conversion page. This conversion page is displayed to the customer after the conversion is made. Note that more than one conversion can be made per click. Cost per conversion is measured by dividing the overall campaign costs by the number of conversions, resulting in the following formula:
Cost per conversion = campaign costs / # of conversions
In the above formula, campaign costs refers to the budget a company spends on SEA. The number (#) of conversions element displayed in the formula can also be seen as a metric, and speaks for itself. The metrics used by Ghose & Yang (2009) are the basic metrics (clickthrough rate, conversion rate, cost per click, and rank) in this research, complemented with the above clarified cost per conversion (Rutz & Bucklin; 2008) and number of conversions. Also the metrics “number of impressions” (or simply called “impressions”) and “number of clicks” will be added, and will be explained later in this section (“click metrics”). There seems to be some overlap between the metrics and it is difficult to make a clear distinguishing the metrics, because they seem to influence eachother in some cases. In order to make a separation between the metrics, an emphasis is put on the formulas representing the different metrics, that all seem to differ. A company spending a considerable budget on SEA should be aware of these differences. Besides cost per conversion, companies can also use conversion rate as a metric. Conversion rate is calculated by dividing the number of conversions through the number of clicks (Ghose & Yang; 2009), resulting in the following formula:
Click metrics
In the displayed formula explaining conversion rate, the number (#) of clicks refers to the number of clicks a website receives. The amount of clicks a website receives can also be seen as an internet marketing metric. Besides following a conversion campaign, businesses choosing for a click campaign are more interested in clicks. This campaign is often used by businesses that sell advertisement space on their website. Businesses using click campaigns want to gather as much traffic to their website for as little money as possible. Companies focussing on click campaigns are mainly interested in cost per click (CPC). Cost per click (sometimes referred as “pay per click”) can be calculated by dividing the overall campaign costs by the number of clicks, resulting in the following formula:
Cost per click = campaign costs / # of clicks
For businesses following a click campaign a high clickthrough rate (CTR) is important, although there was also a connection found between CTR and conversion rate (Yang & Ghose; 2010). Based on the formula CPC drops when the number of clicks is increasing. Websites with a high CTR are seen as relevant by customers, resulting in a lower CPC. In other words, the more people click on your advert, the lower the cost per click will be. Finally, CTR is calculated by dividing the number of clicks by the number of impressions (Brooks; 2004). “Number of impressions” (or “exposure”) is the next internet marketing metric discussed in this research, and is based on the number of times the consumer actually searches on a specific keyword and thus comes in contact with the advert (Ghose & Yang; 2008). To make this more clear, Brooks (2004) states:
“Let’s say you are bidding on the 5th rank for the phrase ‘search engine marketing’ in Google AdWords and someone searches for that phrase in AOL. Google provides results to AOL, so you should get an impression, right? Not necessarily. You will only get an impression if the user clicks through to the second page of search results because only the top few positions are displayed on the first page of search results”
Based on above discussion, the formula of clickthrough rate (CTR) can be formulated as follows:
Rank
Finally, it would be interesting to involve an advertisers position, defined as rank (Yang & Ghose; 2010), on the SERP. This internet marketing metric is easily observable and may also influence (financial) outcomes. This is assumable because Brooks (2005) finds that conversion rates are highest at top ranks. On the other hand, Agarwal (2008, from Ghose & Yang; 2009) states that “nonserious buyers often click on the top slots but do not purchase”, implying that rank is not necessary important in determining financial outcomes. Like mentioned in section §2.1.2 (“Search engine advertising”) rank is determined on the amount an advertiser wants to pay and on the Landing Page Quality Score. The Landing Page Quality Score in particular is hard to measure (Ghose & Yang; 2009), but CTR may be used as a proxy for this score (Yang & Ghose; 2010), implying that the higher the level of CTR is, the higher the Landing Page Quality of that given page is.
In their research on the spillover effects in SEA, Rutz & Bucklin (2008) did not found rank to be significant. Hence, Ghose & Yang (2009), and Yang & Ghose (2010) found that the highest positions in SERP are not neccessary the most profitable ones. Profits seem to be higher on the middle positions than at the top of bottom ones. This research will show if rank in SEA can be characterized as an important metric in influencing outcomes (financial and owner satisfaction).
Concluding this section, different internet marketing metrics (number of impressions, number of clicks, clickthrough rate, cost per click, rank, number of conversions, conversion rate, and cost per conversion) can be distinguished that, on first sight, seem to have some overlap. However, based on the discussed formulas, a clear distinguishing was made between the different internet metrics. The goal of the presented formulas is to clearify the differences between the internet metrics. The presented formulas will not be taken into further calculations, as companies that are actively using SEA as a marketing tool should be aware of these different internet marketing metrics (and their underlying formulas). In addition, the calculation of the different internet metrics for every responding company goes beyond the scope of this research.
§2.1.6 Firm strategic orientation: entrepreneurial orientation (EO)
business strategy. To dig deeper into the role of strategy in small businesses, Megicks (2007) shows some interesting insights. According to Megicks (2007) small retail strategies can be formulated as functional and business strategies. Examples of functional strategies are offering high quality merchandise, and focussing on online and direct marketing. Examples of business strategies are a customer service focus, low cost, and channel expansion. Logically, the functional strategy deals with the day-to-day business, where the business strategy reflects the strategic direction of the company. Small retail businesses often focus on the functional strategy, thereby neglecting the major determinant of long-term success (Megicks; 2007). Megicks (2007) findings indicate a clear superiority of business level strategy over functional level strategy in influencing business success. In other words, choosing a business level strategy leads to better performance.
Therefore, the dimension of competitive aggressiveness is not included as an appropriate proxy for EO. An emphasis will be put on the dimensions innovativeness, proactiveness, and risk-taking, and the measurement items that go along with these dimensions.
§2.1.7 Firm strategic orientation: small business orientation (SBO)
§2.2 Hypothesis formulation
§2.2.1 Marketing performance measurement relationship with outcomes
O’Sullivan & Abela (2007) found that a company’s MPM positively influences the outcomes owner satisfaction with marketing and financial performance. Although their research was held under a different sample, one can imagine that MPM is equally important for SMEs. A SME with high MPM is more likely to track and critically evaluate performance, and can proactively act on observed signals. Such an proactive company will likely gain better financial results than a company that is not capable of measuring marketing performance in that way. Therefore, the first hypothesis can be formulated:
H1a: There is a positive relationship between a SMEs marketing performance measurement ability and a firm’s financial performance.
Besides the proposed relationship between MPM and a SMEs financial performance, a relationship may also exist between MPM and owner satisfaction with marketing. In their research under high-tech businesses in the United States, O’Sullivan & Abela (2007) found a significant positive relationship between MPM ability and CEO satisfaction. Their study was the first study demonstrating such a relationship. In the Dutch SME market, it is also likely that a SMEs MPM ability positively influences owner satisfaction. An owner that is “able to assess its marketing performance using different metrics, should outperform those that lack this ability” (O’Sullivan & Abela; 2007), and should therefore be more satisfied with its marketing performance, and marketing stature in the firm. Based on this discussion, the following hypothesis can be formulated:
H1b: There is a positive relationship between a SMEs marketing performance measurement ability and the owner’ satisfaction with SEA
2.2.2 Internet marketing metrics relationship with outcomes
conversions is positively related to financial performance. In addition, an owner that is satisfied with the financial outcomes of its SEA investment, is also expected to be satisfied with the SEA investment. When comparing the conversion type of metrics with the click metrics (number of clicks, clickthrough rate, cost per click), it is unclear whether there is a relationship with a company’s outcomes. One may say that there could be an indirect relationship between number of clicks and (financial) outcomes, because the more visitors, the more likely conversions will be made. However, one can only assume this effect, because also aspects like landing page quality (or for example product quality) may play a role in determining the number of sales (conversions) made by a company. Therefore, the following hypotheses are formulated:
H2a: There is a positive relationship between conversion internet metrics and a firm’s financial performance
H2b: There is a positive relationship between conversion internet metrics and the owners satisfaction with SEA
§2.2.3 Entrepreneural orientation relationship with outcomes
Hence, in his research of the effect of EO on export (financial) performance, Mostafa et al. (2006) states that firms with high EO are more committed to the internet a have better (export) performance. Based on the discussion above, the following hypotheses are formulated:
H3a: There is a positive relationship between the level of entrepreneurial orientation and a firm’s financial performance
H3b: There is a positive relationship between the level of entrepreneurial orientation and the owners satisfaction with SEA
§2.2.4 Small business orientation relationship with outcomes
In formulating clear hypotheses between SBO and firm outcomes it is essential to look back at the discussion explaining the concept of SBO. Based on that discussion, one can assume that a high level of SBO hinders financial performance, because financial goals are not prioritized and personal goals play an important role for SME owners. However, based on Runyan (2008), a strong emotional attachment to a business may also have a positive effect on performance. Runyan (2008) even showed that the impact of SBO on (financial) performance may be higher than the effect of EO on (financial) performance. In his research, Runyan (2008) focussed on the financial performance outcomes. In this research, also the non-financial outcomes will be taken into account. Based on above discussion, the following hypotheses are proposed:
H4a: There is a positive relationship between the level of small business orientation and a firm’s financial performance
§2.3 Research model
Based on the extensive literature review and hypothesis formulation above, several relationships can be formulated and presented in a conceptual model. First of all, it is expected that MPM influences outcomes for SMEs, resulting in hypothesis 1a and 1b. In addition, this thesis implies that some metrics may influence outcomes more than others. Therefore, conversion metrics are expected to positively influence outcomes, resulting in hypothesis 2a and 2b.
Based on the literature review, it is hypothesized that there is a positive relationship between a firm’s strategic orientation and the firm’s outcomes, resulting in hypothesis 3a and 3b for the relationship between entrepreneurial orientation and outcomes, and hypothesis 4a and 4b for the relationship between small business orientation and outcomes. An entrepreneurial orientated company, may be more innovative which results in high outcomes. It would be interesting to test whether there is a difference between companies of both the strategic orientations. The above explained relations result in the following conceptual model.
§3 METHODOLOGY
In this paragraph, the methods used in this research are described. As a guideline the methods described by Vaccaro et al. (2011) and Zhou (2012) are followed. First, the research setting and data collection procedure is clarified. Second, the items to measure the constructs are given and academic sources are presented. In addition, the method for data analysis is presented.
§3.1 Sample and data collection
In this research, data is collected using an online questionnaire (for the questionnaire, see appendix A) distributed under SMEs conducting SEA. To find these relevant companies, the search engine Google is used. In first instance, search queries containing keywords that focus on retail, travel and finance are generated and companies performing SEA will appear in the SERPs. To increase response, these companies are first contacted by phone, and (owners) permission is asked to send the survey by e-mail. In addition, to increase response, subjective outcomes measures are used. According to Runyan (2008), in previous studies (i.e. Droge, Jayaram, and Vickery; 2004), these subjective measures were found to be highly correlated with objective measures. Hence, as an incentive (Dillman; 2000, from Runyan; 2008) to fill in the survey, the responding company is able to give permission to receive a summary of the most important results from the research.
firm. In this sample, self-employed companies account for 26.7%, followed by small companies with 10-49 employees (12.4%), and medium companies with 50-99 employees (1.9%).
The age of the SME owner is in most cases between 40-49 (37.1%), followed by owners between 30-39 of age (24.8%). Only one percent of SMEs was led by owners younger than 20 years of age. The “student” population (20-29 years old) accounts for 9.5% of total sample size. An overview of sample distribution can be found in appendix C.
§3.2 Measures and validation of constructs
Scale construction
Each construct will be measured by several items (see table 1). Respondents are given the opportunity to fill in answers using a seven-point Likert scale. There is chosen for a Likert scale, because previous academic work (e.g. Runyan; 2008, O’Sullivan & Abela; 2007, Megicks; 2001) showed good results using this scale. There is chosen for a seven-point scale, because most of the important concepts emphasized in this research, were also measured using a seven point scale in prior research (Runyan; 2008, O’Sullivan & Abela; 2007). Changing this scale could therefore harm results. The measurement items that are measuring the construct of outcomes range from very unsatisfied (“zeer ontevreden”) to very satisfied (“zeer tevreden”). The measurement items that are measuring the concept of strategic orientation (EO and SBO) range from totally not agree (“zeer mee oneens”) to totally agree (“zeer mee eens”). The measurement items that account for “ability to measure marketing performance” and the “ability to generate marketing metrics” range from completely unable to (“totaal niet in staat”), to completely able to (“zeer goed in staat”). Finally, the measurement items measuring “type of internet marketing metric used” range from very unimportant (“zeer onbelangrijk”) to very important (“zeer belangrijk”).
Above explanation of measurement items used, shows that construct validity is ensured. According to Garver and Mentzer (1999) construct validity refers to “the degree to which a scale measures what it intends to measure”. By using measurement items that were validated in prior studies, construct validity is safeguarded. An overview of the measurement items used in this research can be found in appendix D.
need to be above 0.70. Because, in most cases, several items are used to measure a particular construct, this first step enables the researcher to drop irrelevant items. In addition, an explanatory factor analysis is conducted to see whether the constructs tested in this research are tested using relevant measurement items.
Dependent variables
To start of with the dependent variables of this research, financial performance (FP), and owner satisfaction with SEA (OS). First of all, FP is divided into two four-item scales measuring financial performance last year, compared to previous years (FLY), and financial performance compared to competition, or FC (Runyan; 2008). The items representing FLY and FC involve items concerning turnover, profitability, ROI and overall performance. There items are mainly based on work of Runyan (2008) and Keh et al (2007), supported by academic work of Megicks (2007, 2001) and O’Sullivan & Abela (2007). Besides the financial performance construct, also owner satisfaction with SEA is characterized as an important outcome in this research. This may be especially important in cases when business are self-employed and have a low EO, in which personal goals play an important role. In these cases especially, owner satisfaction with SEA may shed light on the non-financial performance of small businesses. OS is divided into two one-item scales regarding SEA satisfaction last year, compared to previous years (SLY), and SEA satisfaction compared to competition (SC). The one-item scale that measures SLY and SC is based on academic work of O’Sullivan & Abela (2007), Runyan (2008), and Keh et al. (2007). All the above mentioned dependent variables show high Cronbach’s scores (above 0.900). For a clear overview of the six dependent variables described above in combination with Cronbach’s alpha and explanatory factor analysis scores, see appendix E.
Independent variables
Table 1: Independent variables (Cronbach’s alpha and factor analysis scores)
Construct and scale Details Measurement item Cronbach Alpha Factor 1 Factor 2
MPM* Ability to measure marketing performance 1.Ability to measure SEA results 0,838 0,814
Likert scale (1-7) Ability to generate metrics
2.Ability to generate metrics for financial results SEA (i.e.
CPC, sales figures through online ad) 0,786
totally not able to - totally able to
3. Ability to generate metrics for non-financial results SEA
(i.e. customer satisfaction, customer loyalty) 0,758
4. Ability to generate metrics to benchmark current SEA
results with plans 0,826
5. Ability to generate metrics to benchmark own SEA
results with SEA results of competition 0,708
Entrepreneurial Orientation (EO)* Proactivity
1. In dealing with competitors initiates actions which
competitors then respond to 0,860 (items 1 to 9) 0,525
Likert scale (1-7)
2. In dealing with competitors is very often the first
business to introduce new products/services 0,891
totally not agree - totally agree
3. In general, the top managers of my firm have a strong tendency to be ahead of others in introducing novel ideas
or products. 0,883
Innovativeness
4. In general, the top managers of my firm favor a strong emphasis on R&D, technological leadership, and
innovations. 0,616
5. Very many new lines of products/services last years 0,709
6. Changes in product or service lines have usually been
quite dramatic. 0,854 (items 1 to 6) 0,664
Risk-taking
7. A strong proclivity for high risk projects (with chances of
very high returns). 0,803
8. Owing to the nature of the environment, bold, wide-ranging acts are necessary
to achieve the firm’s objectives. 0,766
9. When confronted with decisions involving uncertainty, my firm typically adopts a bold posture in order to
maximize the probability of exploiting opportunities 0,741 (items 7 to 9) 0,762
Small Business Orientation (SBO)* Purpose and Goals
1. I established this business because it better fit my
personal life than working for someone else. 0,733 0,767
Likert scale (1-7) Emotional Attachment
2. I consider this business to be an extension of my
personality 0,847
totally not agree - totally agree
3. My goals for this business are interwoven
(interconnected) with my family’s needs 0,649
4. I love my business 0,752
Control variables
In order to get valuable results, it is important to focus on the target group in this research, SMEs conducting SEA. To increase the quality of the results, control variables can help. First of all, the size of the company can help in distinguishing SMEs from bigger companies. Therefore, in the survey the EU definition (and appropriate scale) of SMEs is used to distinguish this group. “firm size” as a control variable focusses on the number of employees active in the company. Although also other aspects (e.g. turnover8, or revenue, see O’Sullivan & Abela; 2007) may be important in defining the size of a company, in this research an emphasis is put on size, because owners may be reluctant in providing sensitive financial data. The second control variable used in this research is “firm sector”. Like mentioned earlier, the sample consists of SMEs active in different sectors. Therefore, a control variable that distinguishes between sectors can help in connecting the search queries with the responding companies. More important, results may well differ between sectors, and generalizing results over sectors may be misleading (Megicks; 2001). Hence, by making results sector specific, it offers firms an incentive to fill in the survey. In this research, dummies are used to control for the influence of firm sector on the dependent variables.
At last, the control variables of “firm age” and “owner age” are added to the model. First of all, results may differ between companies of different ages. Runyan (2008) already showed that the relationship EO on financial performance was only found significant when the company was younger than 11 years. In addition, the relationship SBO on financial performance was significant for companies older than 11 years. Hence, one can assume that an older company has had the ability to learn (O’Sullivan; 2007) and has therefore better outcomes. On the other hand, older companies are sometimes stuck in patterns, and can be more conservative than new companies. Schumpeter (1934) stated that innovation comes from new companies with entrepreneurial heroes as leaders. In this light, one may assume that younger companies are better performers. Based on this discussion, it is unclear how “firm age” will influence outcomes. It would be interesting to see if, and how, “firm age” influences outcomes. In addition, the control variable of “firm age” can help in deriving valuable data from the survey. Companies that are less than 1 year old, are not able to fill in several questions regarding performance outcomes (last year compared to previous years, and competition). Some of these companies may however fill in these questions. The “firm age” therefore is used to control for this effect.
Somewhat related to “firm age” is “owner age”. Although “owner age” is sometimes mentioned in papers (e.g. Runyan; 2008), it is unclear how “owner age” influences outcomes. This research will involve “owner age”, because the relatively new marketing instrument of SEA may resolve in company owners also being relatively young. Therefore, it would be interesting to control for the influence of “owner age” in this research.
Concluding this sector, this research will test the influence of firm size, firm sector, firm age, and owner age on SME outcomes.
Assessment of common method bias
First of all, it was important to ensure content validity. Therefore, it was important to base the measurement items used on existing (non-Dutch) literature, and to adapt them to the context under study. In this case, Dutch companies using SEA as a promotional tool. The target group consists of SMEs active in the Dutch SEA market. This research will most likely also involve SME owners that do not have great understanding of the English language. Therefore, the measurement items used in this research are translated from English to Dutch. This is adviceable, because owners with a lack of understanding of the English language can also provide valuable data.
Data analysis
In order to test for the hypothesized relationships, several statistic tests are needed. First, it is essential that the average score of the constructs is calculated. This enables the usage of several tests such as the Pearson correlation test and the univariate and multi-regression analysis. In order to examine the relationships between the seperate constructs, a Pearson Correlation test is performed (O’Sullivan; 2007, Lumpkin; 2001). Wong and Hiew (2005) give an indication on how to render the outcomes of the Pearson Correlation test. They state that a relationship between 0.10 to 0.29 is considered weak, from 0.30 to 0.49 is considered medium, and 0.50 to 1.0 is considered strong. Multicollinearity may occur when the correlation coefficient is stronger than 0.8 between the different independent constructs. Multicollinearity refers to two independent variables that have such a high correlation score, that discrimination between the two is difficult.
Following the correlation test, a regression analysis is performed. In this case, a univariate (or linear) regression analysis is done to test the individual influence of MPM, EO and SBO on outcomes. This test helps to specify the nature of the relationship between an independent and dependent variable and may help in predicting dependent variable values9. The univariate regression formula used in this research is:
y = bX + a
The letter “y” stands for the predicted variable, where “b” stands for slope and “x” for a given value of an independent variable. Finally, the letter “a” stands for the intercept point of the regression line with the y axis.
Also, several multivariate regression analyses are performed to see the relative importance of the independent variables on the outcomes. In addition, a univariate (single item) and multivariate (items in combination with eachother) regression analysis is done to test the influence of the internet marketing metrics on the dependent variables. In this research, model formulation is not the main goal and the combination of the independent variables will most likely not result into high explained variance. Therefore, there is a focus on output with regard to relative importance.
§ 4 RESULTS
In this paragraph the results of the research are presented. An emphasis will be put on the quantitative results from the survey. First of all, descriptives will be presented. Second, the correlation tests conducted in this research will be clarified. And third, regression analysis results are presented.
§4.1 Descriptives
The questionnaire involved one multi-answer question that was focussing on a firm goal in conducting SEA. Several goals for conducting SEA can be formulated, and the multi-answer question “firm goal” was involved, to shed some light in why firms are placing paid advertisements on Google. Based on the results from the survey, most SMEs in want to create online conversions for their own company (92), followed by a distance with SMEs that want to create offline conversions for their own company (21). Only three companies are focussing on third party conversions, and also only three companies want to create brand awareness with their SEA investment. When looking in detail, quite a number of companies are combining goals. For example, in total ten respondents want to create both online and offline conversions with their SEA investment. In table 2 an overview of the “firm goal” results can be found.
Table 2a: firm goal destriptives
Firm goal (multi-answer variable) # Details #
Online conversions for own company 92 Online conversions for own company 79
Online conversions for own company and third party
conversions 1
Online conversions for own company and offline conversions
own company 10
Online conversions and brand awareness 2
Third party conversions 3 Third party conversions 2
Online conversions for own company and third party
conversions 1
Offline conversions own company 21 Offline conversions own company 10
Online conversions for own company and offline conversions
own company 10
Offline conversions own company and brand awareness 1
Brand awareness 3 Online conversions and brand awareness 2
§4.2 Correlation between constructs
Like mentioned in the methodology section of this thesis, based on the Cronbach’s alpha scores, means are calculated for further calculations. Before further testing, it is needed to test if the data is normally distributed. This is done by using the Kolmogorov and Lilliefors test for normality. In this case, it looks that most of the metric variables are not normally distributed, where the independent constructs look normally distributed. To test this effect further, normality plots were calculated. Based on these plots, all data seem to be normally distributed (see appendix G). Therefore, to test for correlation between the independent constructs, the Pearson correlation test can be used. It is important to check whether multicollinearity occurs between the independent constructs. Multicollinearity occurs when two variables are closely correlated, making discrimination between the two difficult. In research, generally a score of 0.8 or higher may indicate multicollinearity (Wei; 2009). Correlation scores of 0.10 to 0.29 are considered weak, scores of 0.30 to 0.49 are considered medium, and scores of 0.5 and higher are considered strong (Wei; 2009). In this research, the Pearson correlation test will be used to test for multicollinearity between the independent variables. The Pearson correlation test shows that no multicollinearity between the independent variables/constructs occurs (see table 2b).
Table 2b: Pearson scores for constructs MPM, EOPI, EORT and SBO
In addition, when focussing on the multicollinearity scores, the Pearson correlation test shows that it is appropriate to divide EO into two subgroups (“EO proactive and innovativeness” and “risk-taking”). As displayed in table 2c, highest correlation is seen between “EO proactive and innovativeness” and “EO risk-taking” (0.469), which can be considered as only medium correlation.
Table 2c: Pearson scores for constructs MPM, EOPI, EORT and SBO
Construct/variable MPM EOPI EORT SBO
MPM 1
EO proactive and innovativess (EOPI) 0,354** 1
EO risk-taking (EORT) 0,390** 0,469** 1
SBO 0,030 0,244* 0,080 1
** = correlation significant at 0,01 level * = correlation significant at 0,05 level
N = 105
Construct/variable MPM EO SBO
MPM 1
EO 0,421** 1
SBO 0,030 0,214* 1
** = correlation significant at 0,01 level * = correlation significant at 0,05 level
Besides the independent variables mentioned above, it is also essential to test for multicollinearity along the different internet metrics tested (“type of marketing metric). Based on the results from the survey, there seems to be multicollinearity between the conversion metrics (number of conversions, conversion rate, and cost per conversion). Although the Pearson correlation scores between cost per conversion and number of conversions is not exceeding 0.800, a high score of 0.797 may well indicate multicollinearity. In addition, the other Pearson correlation scores involving conversion metrics indicate multicollinearity (CR --> NCV 0.903, CCV --> CR 0.834), see table 2d for an overview (Numbers coloured red may indicate multicollinearity).
Table 2d: Pearson scores for type of internet marketing metric
Because multicollinearity between the conversion metrics was observed, discrimination between the different conversion metrics may be difficult for responding companies. Therefore, a single conversion metric was calculated based on the mean scores of NCV, CR and CCV combined. The new Pearson correlation scores indicate that with this new metric involved, no multicollinearity is observed (see table 2e). In further calculations, the metric “conversions” will therefore be used.
Table 2e: Pearson scores for type of internet marketing metric final
Variable I NC CTR CPC RA NCV CR CCV
Importance Number of Impressions (I) 1
Importance Number of Clicks (NC) 0,515** 1
Importance Clickthrough rate (CTR) 0,399** 0,625** 1
Importance Cost per Click (CPC) 0,347** 0,643** 0,539** 1
Importance Rank (RA) 0,443** 0,577** 0,566** 0,482** 1
Importance Number of Conversions (NCV) 0,320** 0,301** 0,485** 0,377** 0,330** 1
Importance Conversion rate (CR) 0,329** 0,300** 0,473** 0,412** 0,374** 0,903** 1
Importance Cost per Conversion (CCV) 0,324** 0,189 0,384** 0,396** 0,367** 0,797** 0,834** 1 ** = correlation significant at 0,01 level
* = correlation significant at 0,05 level N = 88
Variable I NC CTR CPC RA CON
Importance Number of Impressions (I) 1
Importance Number of Clicks (NC) 0,515** 1
Importance Clickthrough rate (CTR) 0,399** 0,625** 1
Importance Cost per Click (CPC) 0,347** 0,643** 0,539** 1
Importance Rank (RA) 0,443** 0,577** 0,566** 0,482** 1
Conversions (CON) 0,342** 0,279** 0,473** 0,417** 0,342** 1
§4.3 Regression analysis
In order to test the hypotheses formulated in the theoretical paragraph of this thesis, the regression analysis can be used. The regression analysis offers the ability to test the influence of an independent variable on a dependent variable (Aiken and West; 1991). In this research, both univariate regression and multivariate regression analyses are conducted. Univariate regression is used to calculate a single independent variable influence on a dependent variable. Univariate regression can be used to specify the nature of the relation between the two variables. Summarized, one can say, that given the value of one independent value, how can one predict the value of the dependent variable? (in other words, the probability of the relation)10.
Multivariate regression is used to test the influence of several independent variables on a single dependent variable. Multivariate regression can help in formulating a model that shows the explained variance of the combination of independent variables in predicting dependent outcomes. In addition, multivariate regression offers the possibility to involve control variables. When performing a multivariate regression analysis, it is important that the effect of multicollinearity is taken into account, because several independent variables are tested in combination with eachother. When performing a multivariate regression analysis, the tolerance indicator needs to be greater than 0.1, and the VIF values need to be less than 10, to ensure that no multicollinearity occured. In the calculations below, significance is leveled on a p-value of 0,05, and a p-value of 0.10.
§4.3.1 Univariate regression
To start of with the univariate regression, hypothesis 1a and 1b are tested. The influence of MPM on the different dependent variables is significant. Based on the univariate regression scores, MPM positively influences FLY (+0.273), FC (+0.254) and financial performance (FP) overall (+0.224), and also a positive relationship was found between MPM and SLY (+0.461), SC (+0.374) and owner satisfaction (OS) overall (+0.408). Based on the results of the univariate regression, hypothesis 1a (MPM positive relationship on FP) and 1b (MPM positive relationship on OS) are supported.
With regard to hypothesis 2a (conversion metrics positive relationship on FP) and 2b (conversion metrics positive relationship on OS), a positive relationship was found between the internet metric number of impressions and the dependent variables of FC (+0.199) and SLY (+0.219). Also
clickthrough rate (CTR) was found to be positively related to SLY (+0.275). Finally, the hypothesized metric conversions was found to be positively related to FC (+0.300). There was no relation found between conversions and OS, and there was only partially empirical evidence found for a relationship between the conversion metrics and the dependent construct of FP (only FC was found significant). Based on above, hypothesis 2a and 2b are rejected. An overview of the univariate regression involving the internet metrics is found in table 3a.
Table 3a: univariate regression scores type of internet marketing metric
Based on the univariate regression scores, EO was positively related (= 0.05) to all of the dependent variables of FLY (+0.327), FC (+0.443), FP(+0.391), SLY (+0.477), SC (+0.770) and OS (+0.689). In addition, when looking deeper in the influence of EO on the dependent variables, EOPI is also found relevant on all the dependent variables. However, EO risk-taking was only found significant related (= 0.10) to SLY (+0.233), SC (+0.301) and OS (+0.274). These results show that EO
Variable B Constant t sig
Impressions FLY 0,066 4,284 0,654 0,515 FC 0,199 4,249 1,761 0,084* FP 0,169 4,226 1,565 0,123 SLY 0,219 3,258 1,877 0,064* SC 0,140 3,552 0,956 0,344 OS 0,156 3,499 1,124 0,267
Number of clicks FLY 0,082 4,110 0,621 0,536
FC 0,147 4,269 0,926 0,358 FP 0,159 4,045 1,041 0,302 SLY 0,201 3,128 1,271 0,207 SC 0,057 3,843 0,290 0,773 OS 0,053 3,893 0,286 0,776 CTR FLY 0,034 4,351 0,264 0,792 FC 0,113 4,455 0,727 0,471 FP 0,141 4,141 0,943 0,350 SLY 0,275 2,688 1,804 0,075* SC 0,024 4,021 0,128 0,898 OS 0,011 4,123 0,064 0,950 CPC FLY 0,006 4,521 0,049 0,961 FC 0,130 4,342 0,867 0,390 FP 0,102 4,343 0,704 0,484 SLY 0,198 3,144 1,237 0,220 SC 0,142 3,396 0,734 0,466 OS 0,140 3,449 0,756 0,453 Rank FLY -0,026 4,671 -0,184 0,855 FC 0,081 4,583 0,514 0,609 FP 0,093 4,354 0,617 0,540 SLY 0,040 3,998 0,232 0,817 SC 0,079 3,780 0,372 0,712 OS 0,075 3,790 0,363 0,718 Conversions FLY 0,011 4,461 0,085 0,933 FC 0,300 3,333 2,240 0,029** FP 0,215 3,655 1,635 0,108 SLY 0,152 3,374 1,020 0,311 SC 0,211 2,903 1,024 0,311 OS 0,185 3,085 0,950 0,347
Controlled with firm age >1
