Empirical Investigation of Factors That Influence Website Performance

Empirical Investigation of Factors That Influence Website Performance

Mahesh S. Raisinghani (Texas Woman's University, USA)
Copyright: © 2021 |Pages: 16
DOI: 10.4018/IJTHI.2021100102
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Abstract

Rapid advancements in digital technology have had a significant influence on businesses' websites. Organizations with well-designed websites have the potential of attracting customers, generating revenues, and increasing market share. Nevertheless, many organizations that are investing billions of US dollars on websites and page development are not attracting customers or generating revenues, incomes, and profits as expected. The study focuses on bounce rate as a key performance measure of website effectiveness. The research focus is on the factors that influence bounce rate to provide insights to advertisers and websites' designers to predict the effectiveness and quality of online advertisements before these ads are shown to both web users and online visitors. The authors investigated the influence of page view, unique page view, average time on page, entrances, and percent exit on bounce rate. The study shows that unique pageviews, average time on page, entrance, and percent exit have a positive and significant effect on bounce rate and have practical and research implications.
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1. Introduction

Websites are among the most innovative technological applications that have appeared in the last 25 years. The World Wide Web using the Internet platform has evolved as an essential mechanism for inter- and intra-organization information exchange (Tarafdar & Zhang, 2008). The last decade has seen media delivery systems converge within single technologies such as mobile phones, personal computers, smart televisions, iPads, and other digital devices. Moreover, the Internet has led a revolution in e-commerce contributing to e-retailers like Amazon and Play.com having huge economic success in the sale of books and music. Most major commercial companies now have their own websites. This e-commerce trend has also resulted in more choices for consumers leading to increased competition, price reduction and empowered consumers who can compare prices for a large range of products and services.

According to Welling and White (2006), ‘the Internet has been a key driver of corporate marketing during the past ten years.” (p. 655). Significantly, in 2017, the US digital advertising revenue was assessed to be $88billion, a 21.4% rise from the already high 2016 revenue of $72.5billion (IAB 2018)1. Therefore, there are inherent opportunities in increasing the attractiveness of e-commerce websites particularly with the advantages big data presents regarding access to massive data on online users. Performance management and optimization of e-commerce websites, therefore, is becoming a critical issue for effective online marketing (Litman et al., 2017; Saura et. al., 2017).

Companies worldwide harnessing the power of their websites in this Age use cookies and Google Analytics to collect Big Data for activities such as advertising products to millions of customers, getting feedback from customers online on product improvement, selling goods and services to customers, expanding their market reach amongst others. Big Data should, however, mean Big Impact (World Economic Forum, 2012). Such impact would include targeted marketing, business insights, client-based segmentation, sales and marketing opportunity identification, buyer preferences, customer feedback, risk analysis, data access amongst others (Kata et al., 2013; Sagiroglu & Sinanc, 2013). Extraction of valuable data, however, remains a critical Big Data challenge, and businesses gain many advantages by harnessing its capabilities for informed strategic directions and increased operational efficiency (Khan et al., 2014).

There are many ways web users can access webpages and websites: (1) direct traffic, i.e., directly typing the company or organization’s URL, (2) indirect or referral traffic, i.e., linking through other domain websites/links, (3) search traffic, i.e., using search engines such as Google and Yahoo, (4) using a computer mouse to click on the websites/links, and (5) using advance voice recognition and eye movement. Moral et al. (2014) explain that search traffic can be classified into two main groups: (1) paid search traffic and (2) unpaid traffic. Some examples of the leading paid search traffic are Google Adwords, Facebook Ads, Outbrain Amplify, LinkedIn Ads, and Twitter Ads. Several benefits accrue for companies and organizations using paid search engines for their websites/pages: exposure in the top three search engines, immediate traffic, consistent traffic, targeted ads to potential web users and online visitors, access to web users and online visitors worldwide, perceived relevance to web users and online visitors worldwide, positive branding implications, and an ability to track both web users’ and online visitors’ shopping and browsing online behaviors. Also, companies that are using Adwords should implement strategies to determine if the Adwords improve the quality of paid/search traffic compared to the unpaid traffic (Moral et al., 2014).

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