Assessment 2: Case Study Research<\/p>\n
Author\u2019s Name<\/p>\n
Institutional Affiliation<\/p>\n
Assessment 2: Case Study Research<\/p>\n
Chosen Case Study Research Question<\/p>\n
How has the content and appearance of Google changed over time?<\/p>\n
Case Study Selection Rationale<\/p>\n
The case study on the changes in appearance and content of Google Search, Google\u2019 primary website, over the years is selected because of four reasons. Firstly, Google has witnessed the most profound changes in its search algorithm. This multinational technology giant has consistently tweaked and fine-tuned its search algorithm, facilitating the customization of its websites\u2019 features and appearance. This has greatly influenced its website design transformation. Secondly, Google changes the definitions, content, and configurations of Google Search frequently, making it the website that best befits the selected research question. <\/p>\n
Thirdly, this search giant\u2019s primary website provides the best tool for extracting rich snippets of any information and online content that one wishes to search from the Internet (Enginess, 2016). Lastly, this website is selected for the case study research endeavor because it offers the best web experience in universal usefulness and accessibility. This means that with Google controlling virtually every Internet aspect, its website offers exceptional experiences for online information organization, online information search, and information relevance.<\/p>\n
Data Obtainment Methods<\/p>\n
Many data collection methods exist, but those used in collecting data about changes in Google Search\u2019s content and appearance in this case study research are website tracking, web analytics, and online surveys. The website tracking data collection approach entails a non-invasive and in-website data obtainment technique that gathers data based on website traffic, website content ratings, cookie information, website user data points, and other elements. This method is suitable for data collection in this research because it gathers data on website structure, content, language, content user authentication, multiple sites\u2019 services, third party content provisioning, website information architecture, website usability testing, content personalization, and web documents\u2019 style (Bujlow et al., 2015; Ermakova et al., 2018; Nermend et al., 2013). <\/p>\n
The web analytics method collects website-related data using web analytics packages such as Google Analytics. These packages collect data via approaches that leverage log files and JavaScript (Murdock, 2006). The log file method in web analytics collects data by tracking files routinely stored on the target website hosts\u2019 server. The JavaScript method of web analytics-based data collection encompasses leveraging JavaScript code included with the website. The code tracks and collects website-related data and sends it to web analytics service providers from where processed analytics of the website can be sourced (Turner, 2010). Both web analytics-based methods collect data on website traffic (visitor numbers, visit durations, visitor behaviors, visitor activity), content visited, website media, content metrics (benchmarks, key performance indicators, and indices), and other website elements (Booth & Jansen, 2010; Kaushik, 2009). <\/p>\n
The online survey method collects data about the website content and appearance changes either by creating flexible and customizable surveys embedded on the host website or using a third party. Online surveys are a convenient data collection method because they can be administered via multiple channels, including shared links on websites, social media, emails, and webpage popups. The merits of leveraging online survey for collecting data on this website include quick returns, easy access to broad and geographically dispersed populations, and simplified data entry. Other advantages are convenience, flexibility, data collection timeliness and speed, survey customization, ongoing digital research innovations, and inquiry diversity (Evans & Mathur, 2018).<\/p>\n
Ethical Challenges and Implications of Using These Methods<\/p>\n
The principal ethical challenge of using these methods is the data privacy and security concern. Collecting data using the web analytics-based method that relies on JavaScript codes raises privacy issues because it entails placing cookies (first party and third party cookies) on unsuspecting website users\u2019 devices and channeling that data to independent companies for storage and review (Murdock, 2006). Another ethical challenge of using these digital data collection methods is user consent. Placing such cookies on users\u2019 device without their consent can culminate in personal data compromises, leading to anonymity and confidentiality issues relating to personal data (Florea & Florea, 2020; Hand, 2018). <\/p>\n
Data handling is the third ethical challenge. It requires adopting safeguarding practices such as storage location, data encryption, pseudonym usage, and secure server technologies to ensure data regarding the target website stays safe (Facca et al., 2020). Other ethical challenges associated with using these methods include data ownership (influenced by website copyright) and purpose of use specifications. One implication of employing these methods involves verifying the trustworthiness and validity of the website-related data gathered. Other implications entail delineating data collection lawfulness, determining data property explicitness or ownership rights (differentiating between data user and data-owner), and the aspects of data portability, data sharing, and data reuse (Facca et al., 2020; Florea & Florea, 2020; Hand, 2018). <\/p>\n
Research Findings<\/p>\n
The research findings indicates that Google Search\u2019s content and appearance have greatly evolved since its inception in 1998. Between this year and 2001, the website\u2019s appearance included only its registered trademark, simple graphics, and adwords (Ionos, Inc., 2018). Its content incorporated only a few million webpages from the 25 million webpages that constituted the web (Khan, 2018). Between 2001 and 2005, the website\u2019s appearance became more complex to include new elements such as news feeds, synonymy developers, location maps, product promotion features, and stock quotes. The content also advanced, moving from simple keywords to longer and tailored conversations (Khan, 2018). From 2004 to 2009, this website progressed in content and appearance to include features for universal search, sports and movie chronologies, patent development, video, Google search application, weather, and flight (Ionos, Inc., 2018). <\/p>\n
Its content expanded from conversations to camera enabled content following advancements in visual recognition technology. By 2013, the website\u2019s appearance and content were more sophisticated, as they included featured snippets such as Google instance search, search engine optimization (SEO), customized search tools, advanced desktop voice search, knowledge graphing, and image-based search (Ionos, Inc., 2018). Today, Google Search is one of the most advanced websites and search engines, with artificially intelligent search options, image carousels, algorithmic webpage filtering, mobile friendly sites, (Enginess, 2016), multiple machine learning-based Google apps, search engine results organization and updating, and other complex content and appearance developments that allow Google to control sites everywhere.<\/p>\n
Brief Project Expansion Commentary <\/p>\n
I might expand this project in three ways. The first is the integration of other scientific research methods, including quantitative, qualitative, or mix-research methods. The second is by brainstorming the research project with available references (peers, experts, industry specialists, and others) and incorporating their suggestions. The third entials visiting several topical guides on the topic for additional inquiry insights into the project.<\/p>\n
References<\/p>\n
Booth, D., & Jansen, B. J. (2010). A review of methodologies for analyzing websites. Web technologies: Concepts, Methodologies, Tools, and Applications, 145-166. <\/p>\n
Bujlow, T., Carela-Espa\u00f1ol, V., Sol\u00e9-Pareta, J., & Barlet-Ros, P. (2015). Web tracking: Mechanisms, implications, and defenses. ArXiv preprint arXiv: 1507.07872. Arxiv.org Digital Library.<\/p>\n
Enginess. (April 15, 2016). Google\u2019s impact on web design. Enginess. Retrieved May 25, 2021, from https:\/\/www.enginess.io\/insights\/google-changes-web-design.<\/p>\n
Ermakova, T., Fabian, B., Bender, B., & Klimek, K. (2018, January). Web tracking-A literature review on the state of research. In Proceedings of the 51st Hawaii International Conference on System Sciences, 4732-4741. Doi: 10.24251\/HICSS.2018.596.<\/p>\n
Evans, J. R., & Mathur, A. (2018). The value of online surveys: A look back and a look ahead. Internet Research, Doi: 10.1108\/IntR-03-2018-0089.<\/p>\n
Facca, D., Smith, M. J., Shelley, J., Lizotte, D., & Donelle, L. (2020). Exploring the ethical issues in research using digital data collection strategies with minors: A scoping review. Plos One, 15(8), e0237875. Doi: 10.1371\/journal.pone.0237875.<\/p>\n
Florea, D., & Florea, S. (2020). Big Data and the Ethical Implications of Data Privacy in Higher Education Research. Sustainability, 12(20), 8744. Doi: 10.3390\/su12208744.<\/p>\n
Hand, D. J. (2018). Aspects of data ethics in a changing world: Where are we now?. Big data, 6(3), 176-190. Doi: 10.1089\/big.2018.0083.<\/p>\n
Ionos, Inc. (2018). Google search results: The evolution of the SERPs. Ionos Digital Guide. Ionos, Inc. Retrieved May 25, 2021, from https:\/\/www.ionos.com\/digitalguide\/online-marketing\/search-engine-marketing\/the-evolution-of-google-search-results-1998-to\/.<\/p>\n
Kaushik, A. (2009). Web analytics 2.0: The art of online accountability and science of customer centricity. John Wiley & Sons. <\/p>\n
Khan, S. (October 2018). The evolution of Search. Google APAC. Retrieved May 25, 2021, from https:\/\/www.thinkwithgoogle.com\/intl\/en-apac\/consumer-insights\/consumer-trends\/evolution-search\/.<\/p>\n
Murdock, K. (2006). Web analytics: Data collection methods. Practical Ecommerce. Confluence Distribution, Inc. Retrieved May 25, 2021, from https:\/\/www.practicalecommerce.com\/Web-Analytics-Data-Collection-Methods.<\/p>\n
Nermend, K., Shihab, A., & W\u0105sikowska, B. (2013). The application of web-tracking method for research of local government units\u2019 websites utility. Szczecin, Poland. Szczecin University.<\/p>\n
Turner, S. J. (2010). Website statistics 2.0: Using Google Analytics to measure library website effectiveness. Technical Services Quarterly, 27(3), 261-278. Doi 10.1080\/07317131003765910.<\/p>\n","protected":false},"excerpt":{"rendered":"
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