John D’Ambra

64 papers receiving 2.5k citations

John D’Ambra's Hit Papers

Algorithmic bias in data-driven innovation in the age of AI 2021 · 218 citations
2180+1+3Years since publication50100150200

Peers

John D’Ambra
Comparison fields: 5 of 118
  • Information Systems and Management 919
  • Communication 367
  • Organizational Behavior and Human Resource Management 520
  • Marketing 386
  • Management Information Systems 295
Replace Banita Lal with:
Banita Lal United Kingdom
Wen‐Lung Shiau Taiwan
Shirish C. Srivastava France
Jyoti Choudrie United Kingdom
Kai R. Larsen United States
Younghwa Lee United States
Craig Van Slyke United States
Pierre Collerette Canada
Andrew Schwarz United States
Wei‐Tsong Wang Taiwan
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Citations per year

Countries citing papers authored by John D’Ambra

Since Specialization
Citations

This map shows the geographic impact of John D’Ambra's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John D’Ambra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John D’Ambra more than expected).

Fields of papers citing papers by John D’Ambra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John D’Ambra. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John D’Ambra. The network helps show where John D’Ambra may publish in the future.

Co-authors

The 25 scholars most cited alongside John D’Ambra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John D’Ambra Line = papers co-authored together John D’Ambra links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 69 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010310
2
Algorithmic bias in data-driven innovation in the age of AI
Hit paper breakdown →
2021218
3 2013209
4
An evaluation of PLS based complex models: the roles of power analysis, predictive relevance and GOF index
2011190
5 2010160
6 2001142
7 2012136
8 2012116
9 201299
10 199887
11 199873
12 200469
13 200461
14 201261
15 201154
16 201249
17 200948
18 201646
19
User Perceived Service Quality of mHealth Services in Developing Countries
201043
20 201740

About John D’Ambra

John D’Ambra is a scholar working on Information Systems and Management, Sociology and Political Science, Communication, Organizational Behavior and Human Resource Management and Social Psychology, having authored 69 papers that have together received 2.7k indexed citations. Recurring topics across this work include Technology Adoption and User Behaviour (36 papers), Digital Marketing and Social Media (25 papers), Knowledge Management and Sharing (20 papers), Customer Service Quality and Loyalty (14 papers), Team Dynamics and Performance (7 papers), Online and Blended Learning (5 papers), Innovative Teaching and Learning Methods (5 papers) and Diverse Aspects of Tourism Research (4 papers). The work is most often cited by research in Information Systems and Management (919 citations), Communication (367 citations), Organizational Behavior and Human Resource Management (520 citations), Marketing (386 citations) and Management Information Systems (295 citations). John D’Ambra has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Shahriar Akter, Pradeep Ray, Ronald E. Rice, Concepción S. Wilson, Shahla Ghobadi, Zixiu Guo, Babak Abedin, Farhad Daneshgar, Grace McCarthy and Shahriar Sajib. Their work appears in journals such as Journal of the Association for Information Systems, Information & Management, Behaviour and Information Technology, IEEE Transactions on Professional Communication and Electronic Markets.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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