Jalonen_2017_Using Social Analytics for Value Co-Creation in Digitalized Ecosystems
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The document discusses using social analytics for value co-creation in digital ecosystems. It defines social analytics as monitoring, analyzing, measuring, and interpreting digital interactions and relationships between people, topics, ideas, and content on social media. Value is seen as emerging through interactions between actors, rather than being embedded by providers. Social analytics can help analyze cognitive, affective, and behavioral contexts to better understand how value is derived through digital interactions.
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Singapore Management UniversityInstitutional Knowledge at Si.docx
Singapore Management University
Institutional Knowledge at Singapore Management University
Research Collection Lee Kong Chian School Of
Business
Lee Kong Chian School of Business
10-2016
Big data and data science methods for management
research: From the Editors
Gerard GEORGE
Singapore Management University, [email protected]
Ernst C. OSINGA
Singapore Management University, [email protected]
Dovev LAVIE
Technion
Brent A. SCOTT
Michigan State University
DOI: https://doi.org/10.5465/amj.2016.4005
Follow this and additional works at: https://ink.library.smu.edu.sg/lkcsb_research
Part of the Management Sciences and Quantitative Methods Commons, and the Strategic
Management Policy Commons
This Editorial is brought to you for free and open access by the Lee Kong Chian School of Business at Institutional Knowledge at Singapore
Management University. It has been accepted for inclusion in Research Collection Lee Kong Chian School Of Business by an authorized administrator
of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]
Citation
GEORGE, Gerard; Ernst C. OSINGA; LAVIE, Dovev; and SCOTT, Brent A.. Big data and data science methods for management
research: From the Editors. (2016). Academy of Management Journal. 59, (5), 1493-1507. Research Collection Lee Kong Chian School
Of Business.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4964
https://ink.library.smu.edu.sg?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
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https://ink.library.smu.edu.sg/lkcsb?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://doi.org/10.5465/amj.2016.4005
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mailto:[email protected]
1
FROM THE EDITORS
BIG DATA AND DATA SCIENCE METHODS FOR MANAGEMENT RESEARCH
Published in Academy of Management Journal, October 2016, 59 (5), pp. 1493-1507.
http://doi.org/10.5465/amj.2016.4005
The recent advent of remote sensing, mobile technologies, novel transaction systems, and
high performance computing offers opportunities to un.
Singapore Management UniversityInstitutional Knowledge at Si.docx
Singapore Management University
Institutional Knowledge at Singapore Management University
Research Collection Lee Kong Chian School Of
Business
Lee Kong Chian School of Business
10-2016
Big data and data science methods for management
research: From the Editors
Gerard GEORGE
Singapore Management University, [email protected]
Ernst C. OSINGA
Singapore Management University, [email protected]
Dovev LAVIE
Technion
Brent A. SCOTT
Michigan State University
DOI: https://doi.org/10.5465/amj.2016.4005
Follow this and additional works at: https://ink.library.smu.edu.sg/lkcsb_research
Part of the Management Sciences and Quantitative Methods Commons, and the Strategic
Management Policy Commons
This Editorial is brought to you for free and open access by the Lee Kong Chian School of Business at Institutional Knowledge at Singapore
Management University. It has been accepted for inclusion in Research Collection Lee Kong Chian School Of Business by an authorized administrator
of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]
Citation
GEORGE, Gerard; Ernst C. OSINGA; LAVIE, Dovev; and SCOTT, Brent A.. Big data and data science methods for management
research: From the Editors. (2016). Academy of Management Journal. 59, (5), 1493-1507. Research Collection Lee Kong Chian School
Of Business.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4964
https://ink.library.smu.edu.sg?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
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https://ink.library.smu.edu.sg/lkcsb?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://doi.org/10.5465/amj.2016.4005
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
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mailto:[email protected]
1
FROM THE EDITORS
BIG DATA AND DATA SCIENCE METHODS FOR MANAGEMENT RESEARCH
Published in Academy of Management Journal, October 2016, 59 (5), pp. 1493-1507.
http://doi.org/10.5465/amj.2016.4005
The recent advent of remote sensing, mobile technologies, novel transaction systems, and
high performance computing offers opportunities to un.
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BUSINESS_ANALYTICS_ppt.ppt
This document discusses management challenges in creating value from business analytics. It notes that while big data and analytics are increasingly popular, organizations face challenges in leveraging them to create business value. The document presents a research framework that views business analytics as an evolving ecosystem. It also discusses theories around how data can generate new sources of value and the routes through which value is created and distributed among parties. The conclusion identifies several avenues for future research on topics related to business analytics.
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Digital data is increasingly being used to track and analyze human activities like work, learning, and living. This document discusses how the "datafication" of these areas is redistributing responsibilities between humans and algorithms. It explores issues around accountability, control, and transparency when important decisions are made based on data. The author advocates developing new "literacies" to ensure data practices align with public interests and values, and calls for a posthuman perspective that sees humans and technology as deeply entangled.
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Big Data for Creating and Capturing Value in the Digitalized Environment: Unp...
Despite significant academic and managerial interest in big data, there is a dearth of research on how big data impacts
the long-term firm performance. Reasons for this gap include a lack of objective indices to measure big data
availability and its impact, and the tendency of studies to ignore the costs associated with collecting and analyzing
big data, assuming that big data automatically delivers benefits to firms. Focusing on how firms create and capture
value from big data about customers, we use the resource-based view and three dimensions of big data (i.e., volume,
variety, and veracity) to understand when the benefits outweigh the costs. Relying on the number of downloads of
mobile device applications, we find that volume of big data has a negative effect on firm performance. This result
suggests that the “bigness” of big data alone does not ensure value creation for a firm, and could even constitute a
“dark side” of big data. Because big data variety—measured as the number of types of information taken per each
application—moderates the negative effects of big data volume, simultaneous high values of volume and variety
allow firms to create value that positively affects their performance. In addition, high levels of veracity (i.e., a high
percentage of employees devoted to big data analysis), are linked to firms benefiting from big data via value capture.
These findings shed light on the circumstances in which big data can be beneficial for firms, contributing to a better
theoretical understanding of the opportunities and challenges and providing useful indications to managers.
Singapore Management UniversityInstitutional Knowledge at Si.docx
Singapore Management University
Institutional Knowledge at Singapore Management University
Research Collection Lee Kong Chian School Of
Business
Lee Kong Chian School of Business
10-2016
Big data and data science methods for management
research: From the Editors
Gerard GEORGE
Singapore Management University, [email protected]
Ernst C. OSINGA
Singapore Management University, [email protected]
Dovev LAVIE
Technion
Brent A. SCOTT
Michigan State University
DOI: https://doi.org/10.5465/amj.2016.4005
Follow this and additional works at: https://ink.library.smu.edu.sg/lkcsb_research
Part of the Management Sciences and Quantitative Methods Commons, and the Strategic
Management Policy Commons
This Editorial is brought to you for free and open access by the Lee Kong Chian School of Business at Institutional Knowledge at Singapore
Management University. It has been accepted for inclusion in Research Collection Lee Kong Chian School Of Business by an authorized administrator
of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]
Citation
GEORGE, Gerard; Ernst C. OSINGA; LAVIE, Dovev; and SCOTT, Brent A.. Big data and data science methods for management
research: From the Editors. (2016). Academy of Management Journal. 59, (5), 1493-1507. Research Collection Lee Kong Chian School
Of Business.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4964
https://ink.library.smu.edu.sg?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
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https://ink.library.smu.edu.sg/lkcsb?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
https://doi.org/10.5465/amj.2016.4005
https://ink.library.smu.edu.sg/lkcsb_research?utm_source=ink.library.smu.edu.sg%2Flkcsb_research%2F4964&utm_medium=PDF&utm_campaign=PDFCoverPages
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mailto:[email protected]
1
FROM THE EDITORS
BIG DATA AND DATA SCIENCE METHODS FOR MANAGEMENT RESEARCH
Published in Academy of Management Journal, October 2016, 59 (5), pp. 1493-1507.
http://doi.org/10.5465/amj.2016.4005
The recent advent of remote sensing, mobile technologies, novel transaction systems, and
high performance computing offers opportunities to un.
Singapore Management UniversityInstitutional Knowledge at Si.docx
Singapore Management University
Institutional Knowledge at Singapore Management University
Research Collection Lee Kong Chian School Of
Business
Lee Kong Chian School of Business
10-2016
Big data and data science methods for management
research: From the Editors
Gerard GEORGE
Singapore Management University, [email protected]
Ernst C. OSINGA
Singapore Management University, [email protected]
Dovev LAVIE
Technion
Brent A. SCOTT
Michigan State University
DOI: https://doi.org/10.5465/amj.2016.4005
Follow this and additional works at: https://ink.library.smu.edu.sg/lkcsb_research
Part of the Management Sciences and Quantitative Methods Commons, and the Strategic
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Citation
GEORGE, Gerard; Ernst C. OSINGA; LAVIE, Dovev; and SCOTT, Brent A.. Big data and data science methods for management
research: From the Editors. (2016). Academy of Management Journal. 59, (5), 1493-1507. Research Collection Lee Kong Chian School
Of Business.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4964
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1
FROM THE EDITORS
BIG DATA AND DATA SCIENCE METHODS FOR MANAGEMENT RESEARCH
Published in Academy of Management Journal, October 2016, 59 (5), pp. 1493-1507.
http://doi.org/10.5465/amj.2016.4005
The recent advent of remote sensing, mobile technologies, novel transaction systems, and
high performance computing offers opportunities to un.
Identifying semantics characteristics of user’s interactions datasets through...
Identifying semantics characteristics of user’s interactions datasets through...Fernando de Assis Rodrigues
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This document outlines opportunities and challenges in crisis informatics, which is an integrated approach to the technical, social, and informational aspects of crises. It begins with definitions of key terms like crisis informatics and crowdsourcing. It then discusses types of social media and ways crowdsourcing is used during crises. Opportunities of crisis informatics include citizen-based hazard science, situational awareness, and damage estimates. Challenges include ensuring data quality, integrating crowdsourced and authoritative data, and addressing legal/policy issues. The document concludes by identifying priority research challenges such as developing validation methods and best practices for data integration.
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This document summarizes Katrien Verbert's research into mixed-initiative recommender systems. It discusses her work on explaining recommendations to increase user trust and enabling user interaction with recommendation processes. Examples of projects include TasteWeights, a visual interactive hybrid recommender, and IntersectionExplorer, which allows users to explore recommendations from multiple perspectives. The document also outlines Verbert's studies on different aspects of interactive recommender systems like transparency, user control, and personalization.
PhD proposal: Specialized heuristics for crowdsourcing website design
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Full Erdmann Ruttenberg Community Approaches to Open Data at Scale
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This presentation was provided by Chris Erdmann of Library Carpentries and by Judy Ruttenberg of ARL during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018. Grounded, data with a story
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Data as a service: a human-centered design approach/Retha de la Harpe
The document discusses the human-centered design approach to data as a service. It emphasizes engaging with communities to understand local contexts and involving stakeholders throughout the research process. The presentation outlines steps for responsible research, including obtaining ethics approval, engaging gatekeepers, sensitizing researchers to cultural practices, and documenting engagement activities. It also discusses challenges around community research fatigue and ensuring information meets recipient needs in terms of being the right information, at the right time, for the right purpose.
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TL_Thompson.pptx.ppt
Digital data is increasingly being used to track and analyze human activities like work, learning, and living. This document discusses how the "datafication" of these areas is redistributing responsibilities between humans and algorithms. It explores issues around accountability, control, and transparency when important decisions are made based on data. The author advocates developing new "literacies" to ensure data practices align with public interests and values, and calls for a posthuman perspective that sees humans and technology as deeply entangled.
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Talk given at the Social Media and the Transformation of Public Space Conference on June 19 at the University of Amsterdam. References and comments are in the notes section.
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Convergence, Computation and Continuity: Challenges for PR in the 21st CenturySimon Collister & Associates
This document discusses the convergence of media platforms, the rise of computational communication, and the continuity challenges this presents for public relations (PR). It notes that algorithms now play a central role in how communication and data are distributed, organized, and made sense of. This shifts some social power away from human PR practitioners towards algorithms. It raises ethical questions around algorithmic responsibility and transparency. However, PR can work with algorithms by creating engaging content tailored to them. Overall, computational communication will require PR to invest in new data and algorithm skills while maintaining human elements.PatternLanguageOfData
This document discusses how human beings can play an important role in making sense of big data beyond just visualization. It presents a case study where students transformed a large dataset into a visual language and "text" that could be interpreted. The document argues that current sense-making models are too technology-centric and that meaningful interpretation emerges from collaboration between algorithms, data, and human beings. Human perceptual abilities allow them to recognize patterns where computers see only numbers.
Big Data for Library Services (2017)
Big Data & DS Analytics for PAARL aims to help library participants relate Big Data and Data Science applications to library services. The speaker discusses Big Data concepts like the 3 V's of volume, velocity, and variety. Library data resources and analytics challenges are presented. Opportunities for libraries in Big Data include expertise in metadata, assessment, and collaboration. Building a Big Data culture requires openness, investment, training, and data sharing standards. Data governance differs from data management. Machine learning and social listening are explored as examples. Trends in data science domains and tools are shared.
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STUDY ON THE DEVELOPMENT STRATEGY OF HUZHOU TOURISM
ABSTRACT: Huzhou has rich tourism resources, as early as a considerable development since the reform and
opening up, especially in recent years, Huzhou tourism has ushered in a new period of development
opportunities. At present, Huzhou tourism has become one of the most characteristic tourist cities on the East
China tourism line. With the development of Huzhou City, the tourism industry has been further improved, and
the tourism degree of the whole city has further increased the transformation and upgrading of the tourism
industry. However, the development of tourism in Huzhou City still lags far behind the tourism development of
major cities in East China. This round of research mainly analyzes the current development of tourism in
Huzhou City, on the basis of analyzing the specific situation, pointed out that the current development of
Huzhou tourism problems, and then analyzes these problems one by one, and put forward some specific
solutions, so as to promote the further rapid development of tourism in Huzhou City.
KEYWORDS:Huzhou; Travel; Development
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UR BHATTI ACADEMY AND ONLINE COURSES.pdf
UR BHatti Academy dedicated to providing the finest IT courses training in the world. Under the guidance of experienced trainer Usman Rasheed Bhatti, we have established ourselves as a professional online training firm offering unparalleled courses in Pakistan. Our academy is a trailblazer in Dijkot, being the first institute to officially provide training to all students at their preferred schedules, led by real-world industry professionals and Google certified staff.
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STUDY ON THE DEVELOPMENT STRATEGY OF HUZHOU TOURISM
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Jalonen_2017_Using Social Analytics for Value Co-Creation in Digitalized Ecosystems
- 1. Using Social Analytics for Value Co-Creation in Digitalized Ecosystems 13th International conference on Technology, Knowledge & Society May 26–27, 2017, Toronto http://techandsoc.com/ Dr. Harri Jalonen www.harrijalonen.fi harri.jalonen@turkuamk.fi www.deeva.fi@Jalonen
- 2. Time Data Available data Ability to analyze data Ability to exploit data KNOWLEDGE GAP DOING GAP • Variety • Volume • Velocity • Value Information landscape IoT Open data Social media Firm-specific data One zettabyte, 10 (DalleMule & Davenport 2017) 21 (Based on Walker 2015)
- 3. BUSINESS EVENT Information received Information analyzed Actions taken Net value Action time INFORMATION LAG ANALYSIS LAG DECISION LAG (Based on Hackathorn 2004) Value Time The value-time curve
- 4. (Based on Grönroos & Voima 2013) PROVIDER VALUE SPHERE JOINT VALUE SPHERE CUSTOMER VALUE SPHERE Production (potential value) Value creation in interaction (real value) Independent value creation (real value) Value-in-exchange Value-in-use Value cannot be embedded in the value provider’s output and captured by price (Vargo & Lusch, 2017).
- 5. (Based on Gao & Feng 2016; Sheth et al. 1991; Swedberg 2004; van Noort et al. 2012; Powell & Roberts 2017; Vargo & Lusch 2017) Value is not an objective feature of the entity neither subjective opinion of the evaluator but a phenomenon that emerges from the interaction. The diversity of value: functional, social, emotional, epistemic & conditional value. Interactivity between the brand and consumers: cognitive, affective & behavioral. Uses & gratification theory: information seeking, entertainment, social interaction, self-expression & impression management.
- 6. (Based on Vargo & Lusch 2017) ACTOR ACTOR ACTOR Value co-creation Value-in-use Value-in-context SOCIAL ANALYTICS? Value is fundamentally derived and determined in use in a particular context (Vargo & Lusch, 2017).
- 7. Social media as ’service’ for enabling interactions between actors. Social analytics is monitoring, analyzing, measuring and interpreting digital interactions and relationships of people, topics, ideas and content. (Gartner IT Glossary) The emergence of value through interaction.
- 8. COGNITIVE CONTEXT: The realm of emotions CONTEXT: The realm of thoughts CONTEXT: The realm of actions FUNCTION: Providing or requesting information WHAT IS ANAYZED: Information and knowledge WHAT IS ANALYZED: Feelings and attitudes FUNCTION: Entertainment & self-expression FUNCTION: Selecting & recommending, social interaction WHAT IS ANALYZED: Choices and decisions AFFECTIVE BEHAVIOURAL SOCIAL ANALYTICS
- 9. Thank you! Q & A? @Jalonen
- 10. References DalleMule & Davenport (2017). What´s your data strategy? Harvard Business Review, 95:3, 112–121. Gao & Feng (2016). Branding with social media: User gratifications, usage patterns, and brand message content strategies. Computers in Human Behavior, 63, 868-890. Hackathorn (2004). The BI watch: real-time to real-value. DM Review, January, 2004. Powell & Roberts (2017). Situational determinants of cognitive, affective, and compassionate empathy in naturalistic digital interactions. Computers in Human Behavior, 68, 137–148. Sheth, Newman, & Gross (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22: 159– 170. Swedberg (2004). What has been accomplished in New Economic Sociology and where is it heading? European Journal of Sociology, 45:3, 313–330. van Noort, Voorveld & van Reijmersdal (2012). Interactivity in brand web sites: cognitive, affective, and behavioral responses explained by consumers' online flow experience. Journal of Interactive Marketing, 26 , 223–234. Vargo & Lusch (2017). On service-dominant logic. International Journal of Research in Marketing, 34:1, 46–67. Walker (2015). From Big Data to Big Profits. Success with Data and Analytics. Oxford University Press, New York, NY.