A model-based approach for capturing consumer preferences from crowdsources: The case of Twitter

Svee, Eric-Oluf; Zdravković, Jelena

doi:10.1109/rcis.2016.7549323

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…This research extends work presented in [3,4,11] as the part of an overall effort to move consumer preferences toward greater model orientation. It considers opportunities for automation related to the development and application of the Consumer Preference Meta-Model to support user preferences according to their valuerelated content and segmentation, as well as the quantitative thresholds set by decision makers.…”

Section: Discussion Conclusion and Future Worksupporting

confidence: 61%

“…CPMM presented in Figure 1 is used to house, classify, and categorize consumer preferences for products or services. CPMM aims to elicit the preferences from different sources of data, as well as to manage them according to different value frameworks as shown in our previous studies [3,4,11].…”

Section: The Consumer Preference Meta-modelmentioning

confidence: 99%

“…This study makes use of the following, differing from [11] which made use of a derived measure called "social weight":…”

Section: Quantitative Measure Calculationmentioning

confidence: 99%

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A Semi-automated Method for Capturing Consumer Preferences for System Requirements

Nguyen

Svee

Zdravković

2016

Lecture Notes in Business Information Processing

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There is a pressing need in the modern business environment for business-supporting software products to address countless consumers' desires, where customer orientation is a key success factor. Consumer preference is thus an essential input for the requirements elicitation process of public-facing enterprise systems. Previous studies in this area have proposed a process to capture and translate consumer preferences into system-related goals using the Consumer Preference Meta-Model (CPMM) used to integrate consumer values from the marketing domain into objectives of information systems. However, there exists a knowledge gap between how this process can be automated at a large scale, when massive data sources, such as social media data, are used as inputs for the process. To address this problem, a case in which social media data related to four major US airlines is collected from Twitter, is analyzed by a set of text mining techniques and hosted in a consumer preference model, and is further translated to goal models in the ADOxx modelling platform. The analysis of experimental results revealed that the collection, recognition, model creation, and mapping of consumer preferences can be fully or partly automated. The result of this study is a semi-automated method for capturing and filtering consumer preferences as goals for system development, a method which significantly increases the efficiency of large-scale consumer data processing.

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Section: Discussion Conclusion and Future Worksupporting

confidence: 61%

Section: The Consumer Preference Meta-modelmentioning

confidence: 99%

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A Semi-automated Method for Capturing Consumer Preferences for System Requirements

Nguyen

Svee

Zdravković

2016

Lecture Notes in Business Information Processing

Self Cite

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“…• Microblog data from twitter, Facebook, and Weibo were used for automated requirements elicitation. Of total eleven studies that used twitter, four studies extracted only texts [64][65][66][67], while the rest extracted additional metadata [68][69][70][71][72][73][74]. The metadata include the number of retweets, likes, lexically similar tweets (i.e., duplicates), twitter followers and friends (i.e., social rank), replies to tweets, as well as hashtags, handles (i.e., indicated by an @ appended with a username), and demographic data of the person who tweeted.…”

Section: Microblogsmentioning

confidence: 99%

“…• A user-oriented conceptual modeling approach was used for conceptualizing potential requirements in the form of consumer preferences and classifying and ranking the preferences to identify software features [73]. Highly-ranked consumer preferences in the conceptual model were further transformed into a i* goal model to explicate early/highlevel system requirements [72].…”

Section: Model-orientedmentioning

confidence: 99%

Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

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Decision-Making in Crowdfunding – The Value of Behavioral Issues in Collaborative Business Environments

Busse

Strauß

Greguš

2019

Lecture Notes in Computer Science

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A model-based approach for capturing consumer preferences from crowdsources: The case of Twitter

Cited by 6 publications

References 13 publications

A Semi-automated Method for Capturing Consumer Preferences for System Requirements

A Semi-automated Method for Capturing Consumer Preferences for System Requirements

Data-Driven Requirements Elicitation: A Systematic Literature Review

Decision-Making in Crowdfunding – The Value of Behavioral Issues in Collaborative Business Environments

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