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Development of an online reputation index

Blankhorst, Sofie (2017) Development of an online reputation index.

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Abstract:Introduction: This study shows the development of an Online Reputation index method. The EIT Digital was used as a case study wherein the new OR-index method was tested. In the last few years the amount of social media users has risen with a fast space, which resulted in the rise of available big social data. This data has been used as input for the index. Reputation studies have been used for quite some time now, however the establishment of an online reputation based on Big Social Data is a new development. Concepts such as Big Data, Business Intelligence and Big Social Data are used very often in organizations. But, there is little scientific research that gives more explanation in these concepts. Moreover, many companies already collect enormous amounts of data, but valuable insights in this sort of online reputation data analysis is yet limited. Research: In this research the following question was formulated: What is a practical, valid and reliable Online Reputation index method for the Higher Education Network? To answer this question, five sub-questions were formulated which gave different perspectives on the development of the OR index. The research investigated which constructs were part of the reputation index and in which way validity and reliability could be tested to investigate to what extent the developed index is quality acceptable index. Development: The Online Reputation index method was developed based on metrics obtained from literature on measuring online reputation. The metrics were then used to determine the reputation within the online domain. In total 6 metrics have been selected: Followers, Visitors, Share of Voice, Reach, Sentiment and Conversation Volume. For each metric, weightings have been determined based on their importance in forming the online reputation. With this information a formula has been developed. By using the formula the OR index can be calculated daily. To provide more insights for the potential patterns within the OR index, knowledge from the economic area was used. In stock exchanges, such as the AEX, patterns exist already for a longer time period. Based on the knowledge on stock patterns, four patterns are defined that could exist in the OR-index. These patterns are: Crossover pattern, Explosive OR pattern, Increased or Decreased OR pattern and OR-Correlation pattern. If one of these four patterns existed in the OR-index, has been tested and investigated in the conduction phase of the index. Results: Based on the results of the OR index it was determined how the online reputation has developed itself for a set time interval by investigating the contexts of the conversations within the chosen time interval. With the results of the context analysis it was possible to identify the cause of a specific reputation trend. One of the results showed that there was a high increase in the OR-index from one day to another. This cause of this was the sudden increase in the amount and reach of neutral conversations around that day. Practical usefulness and recommendations: As this is a development study, the practical usefulness of this method is also relevant. The process of determining the OR-index is not a difficult one, however some implications arose around the practical usability of the developed method at certain steps in the method. First of all, the filtering procedure had its implication in the languages selected. To determine the OR-index, 10 languages have been taken into account, which made it difficult to filter certain conversations for their belonging sentiment. In addition, it became more difficult to conduct context analysis on the conversations since the researcher was only familiar with 2 languages. Another implication in the conduction of context analyses is that this process is done manually. When there are for example 140 conversations in 1 day, then determining the context around each conversation takes a lot of time. It has been recommended to extent the online reputation metrics with other OR metrics in determining the OR index if the additional metrics are reliable and valid which must be investigated by more research. Moreover, it has been suggested to conduct more research on the metrics that in this research lack in content validity. Whenever more research has been conducted on these metrics the decision can be set to omit the metrics when content validity is still not met or to keep the metrics when content validity is met. The last recommendation is to adapt the tooling. In this research two toolings are used, the problem with one of them is that the data triangulation is not possible, the underlying algorithm is unknown and the accuracy of data download availabilities is limited for a time period of 7 days, resulting in a limitation of the reliability and validity of the tooling. Perhaps another tooling could be used that has the ability to download data for a longer time period, the option to conduct data triangulation and has a known algorithm. This contributes to the reliability and validity of the data collection measurement and eventually to the reliability and validity of the OR-index.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Business Administration MSc (60644)
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