Leveraging Data Analytics for Behavioral Research

Motiwalla, Luvai; Deokar, Amit V.; Sarnikar, Surendra; Dimoka, Angelika

doi:10.1007/s10796-019-09928-8

Cited by 19 publications

(9 citation statements)

References 39 publications

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“…Big Data analytics are increasingly used to improve business outcomes as the collaboration resulting from technology has been found to be positively related to financial performance (Hwang and Kim 2019 ). Several new sources of data over the past two decades has resulted in the availability of large amounts of user data in the digitization of commerce (Motiwalla et al 2019 ). Big data sources have helped to enhance supplier insight into user behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Calls for research underpin the importance of behavioral research in examining incentive structures, change mechanisms, and social expectations to motivate the use of big data applications (Motiwalla et al 2019 ). The organizational change affects organizations internally (and externally) through increasing uncertainty, anxiety, stress, and ultimately resistance, which are critical to the success or failure of change programs (Shah et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Stakeholder sentiment in service supply chains: big data meets agenda-setting theory

Cao

Schniederjans

2021

Serv Bus

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With growing reluctance to store and disseminate sensitive data throughout a supply chain network, there is a need to understand sentiment of big data and ways of control to achieve greater economic viability in the service-oriented supply chain which reflect a greater focus on knowledge sharing from traditional supply chains. Social network data were collected after referencing a focal corporate media (CM) document. This study provides causal inference by first conducting a CM document search and then a social network post web scrape of postings that reference the CM document while controlling for time and other demographic variables. This study finds salience of the big data topic positively impacts stakeholder sentiment but not when future applications are discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stakeholder sentiment in service supply chains: big data meets agenda-setting theory

Cao

Schniederjans

2021

Serv Bus

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“…Behavioral research in information systems employing quantitative methods has traditionally relied on mainly survey-based approaches to gather subjective user data. With new advances in technology such as mobile computing, wearable devices, and social media, along with computational capabilities, organizations are in a position to leverage objective data in addressing IT issues typically addressed in behavioral research (Ducange et al, 2018;Motiwalla et al, 2019). DS takes data analysis to the next level, allowing businesses to predict what users might do (Khade, 2016).…”

Section: Taking Consumer Behavior To the Next Level With Dsmentioning

confidence: 99%

Challenges in Decoding Consumer Behavior with Data Science

Chkoniya¹

2020

Eur. j. econ. bus. stud.

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Decoding the ever-evolving consumer behavior is one of the biggest challenges faced by marketers around the world. The future of consumer behavior research is put into question by the advances in data science. Today, when consumers are all the time exposed to new technologies, trends such as facial recognition, artificial intelligence, and voice technology did not advance as rapidly as predicted, marketing intelligence gained a significant share of the spotlight. This paper gives an overview of possible ways to anticipate consumer data intelligence development from the perspectives of a robust data set and deep artificial intelligence expertise for better understanding, modeling, and predicting consumer behavior. Showing that marketing cannot happen in isolation in the era of digital overexposure, it requires a deeper understanding of consumer behavior. Data scientists, analysts, and marketers around the world have to work together to increase consumer loyalty, grow revenue, and improve the predictiveness of their models and effectiveness of their marketing spend. Efficiently integrating consumer behavior data into marketing strategies can help companies improve their approach towards attracting and winning the diverse and dynamic consumer segments and retaining them. This synthesis of current research will be helpful to both researchers and practitioners that work on the use of data science to understand and predict consumer behavior, as well as those making long-range planning marketing decisions.

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“…Indeed, the enormous success of machine learning techniques has consistently shifted the interest of corporate users towards more sophisticated analytical applications (Popovic et al 2018;Schuff et al 2018). In addition, recent research envisions cross-cutting data management, analytics, and artificial intelligence in various sectors, such as applied data science (Chiusano et al 2021), behavioral research (Motiwalla et al 2019) and social impact (Gupta et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cubes with Models to Describe Multidimensional Data

et al. 2021

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The Intentional Analytics Model (IAM) has been recently envisioned as a new paradigm to couple OLAP and analytics. It relies on two basic ideas: (i) letting the user explore data by expressing her analysis intentions rather than the data she needs, and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of interesting model components (e.g., clusters). In this paper we contribute to give a proof-of-concept for the IAM vision by delivering an end-to-end implementation of , one of the five intention operators introduced by IAM. Among the research challenges left open in IAM, those we address are (i) automatically tuning the size of models (e.g., the number of clusters), (ii) devising a measure to estimate the interestingness of model components, (iii) selecting the most effective chart or graph for visualizing each enhanced cube depending on its features, and (iv) devising a visual metaphor to display enhanced cubes and interact with them. We assess the validity of our approach in terms of user effort for formulating intentions, effectiveness, efficiency, and scalability.

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Leveraging Data Analytics for Behavioral Research

Cited by 19 publications

References 39 publications

Stakeholder sentiment in service supply chains: big data meets agenda-setting theory

Stakeholder sentiment in service supply chains: big data meets agenda-setting theory

Challenges in Decoding Consumer Behavior with Data Science

Enhancing Cubes with Models to Describe Multidimensional Data

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