Evaluation of Search Engines: A Conceptual Model and Research Issues

Palanisamy, Ramaraj

doi:10.5539/ijbm.v8n6p1

Cited by 9 publications

(19 citation statements)

References 67 publications

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“…Trust is an external variable to the model and has been defined by previous research in a variety of ways, both theoretically and operationally. Palanisamy [37] demonstrated and developed a model for the acceptance of different search engines and linked the influence of PU with USE. Liaw and Huang [27] studied the influence of PU on ATU to understand users' attitudes towards using search engines and the perceived utility of the different search engines.…”

Section: Technology Acceptance Model (Tam) Variablesmentioning

confidence: 99%

“…To do this, the influence that attitude has on use, when a user trusts the search engine, was measured [44]. Palanisamy [37] also included the Trust variable in the model, in order to find the reliability of search engines and their technological acceptance. Using this research on search engines, we propose the following hypothesis.…”

Section: Complexitymentioning

confidence: 99%

“…Lim and Ting [38], Palanisamy [37], and Hsu and Walter [42] also analysed the influence that Trust, as an external variable, has on PEOU in acceptance models and revealed the influence of both variables for search engines [5,45]. Therefore, the following hypothesis was proposed.…”

Section: Complexitymentioning

confidence: 99%

See 2 more Smart Citations

Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

2018

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This paper analyses the complexity of user behaviour when facing the challenge of using sustainable applications, such as Internet search engines. This paper analyses an acceptance model using extended TAM (Technology Acceptance Model) with Trust as an added external variable. It was suggested that Trust indirectly influences the final Intention to Use with the perceptions of Utility and Ease of Use. To test the proposed model, a survey was carried out with users from different geographical areas of Spain (n=445). The second aim of this study was to understand the complexity of marketing segmentation by separating the application users into different user groups. Users were grouped by their preference of favorite Internet search engine. Unobserved heterogeneity was studied using FIMIX-PLS, and three different user behaviours with search engines were identified. These corresponded to the number of inhabitants who live in the user area. In this way, the impact that the environment has on user choice, acceptance, and use of this type of sustainable applications was shown. The results were checked using PLS-SEM and showed that the model for the adoption of sustainable search engines is explanatory and predictive because confidence and acceptance for this TAM were validated. The conclusions are interesting for developers of environmentally sustainable and responsible applications which want to coincide with current trends to ensure that users prefer them.

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Section: Technology Acceptance Model (Tam) Variablesmentioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

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Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

2018

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“…Hence, the search facility has always been important for website navigation [9] because they enable customers to find the item that they are looking for both easily and efficiently and without physically visiting multiple locations. For small online businesses, measuring the relevance of search results can help understand whether the search facilities that they are using are efficient enough to provide the information that a customer needs [10]. For e-businesses, evaluating the capacity of the search engine is critical because users were found not willing to invest more time or more effort to improve their searching strategies [11].…”

Section: Relevance Of Search Resultsmentioning

confidence: 99%

Successful Data Science Projects: Lessons Learned from Kaggle Competition

Al-Taie

Salim

Obasa

2017

KJAR

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Abstract:The workflow from data understanding to deployment of an analytical model of a data science project begins at framing the problem at hand, a task that is typically business-oriented and requires humanto-human interaction. However, the next three steps: data understanding, feature extraction, and model building that come next in the pipeline are the key to successful data science projects. Failing to fully understand the requirements of each of these three steps can negatively affect the performance of the proposed system. Hence, the current study tries to answer the following question "What are the requirements of a successful data science project?" To answer this question, we will use the solution that we built to measure the relevance of local search results of small online e-businesses and submitted to Kaggle data science platform to shed light on why our solution did not achieve a top position among other competitors. Evaluation of the design that we submitted to the competition is going to be carried out in the spirit of the three winning submissions. Our results revealed that well-performed data preprocessing, well-defined features, and model ensembling are critical for building successful data science projects. Such a clarification provides insight into specific aspects of model design to help others including Kagglers avoid possible mistakes while approaching their data science projects.

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“…Many factors, such as: effectiveness and efficiency are influenced by the use of search engines when surfing the internet. Those factors play a significant role in assessing and in distinguishing between the search engines, in terms of attracting new customers and on the effectiveness of online advertisements (Palanisamy, 2013). We can summarize the impacts of using search engines as follows:…”

Section: Research Problemmentioning

confidence: 99%

The Impact of Using Google Search Engine and Yahoo Bing Network on Attracting New Customers Online and on the Effectiveness of Online Advertisements

Alkarablieh¹

2016

IJMS

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This study aims at measuring the impact of using the most popular search engines: Google and Yahoo Bing networks on attracting new customers towards company websites and on the effectiveness of online advertisements. Quantitative research method and questionnaires were used by researcher to collect data.The researcher distributed (129) questionnaires on (33) companies that use E-marketing in Jordan. Among those only (87) questionnaires were returned and (42) of which only were usable and valid for the statistical analysis. After executing the analysis, the study concluded that Google search engine has a direct effect on attracting new customers and on online advertisements effectiveness at (α ≤ 0.05) level for companies using E-marketing in Jordan, while Yahoo Bing search engine has a direct effect on attracting new customers and on the effectiveness online advertisements at (α ≤ 0.05) level for companies using E-marketing in Jordan.

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Evaluation of Search Engines: A Conceptual Model and Research Issues

Cited by 9 publications

References 67 publications

Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

Successful Data Science Projects: Lessons Learned from Kaggle Competition

The Impact of Using Google Search Engine and Yahoo Bing Network on Attracting New Customers Online and on the Effectiveness of Online Advertisements

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