Peter Lenk

2.8k citations
59 papers · 2k · h-index 21

Impact in

  • Marketing top 1%
    • Consumer Market Behavior and Pricing
    • Consumer Behavior in Brand Consumption and Identification
    • Statistical Methods and Bayesian Inference
    • Statistical Methods and Inference

Papers in

Peter Lenk

56 papers receiving 1.8k citations

Peers

Peter Lenk
Comparison fields: 5 of 115
  • Marketing 539
  • Statistics and Probability 292
  • Economics and Econometrics 516
  • General Decision Sciences 48
  • Management Science and Operations Research 209
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Countries citing papers authored by Peter Lenk

Since Specialization
Citations

This map shows the geographic impact of Peter Lenk'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 Peter Lenk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Lenk more than expected).

Fields of papers citing papers by Peter Lenk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Lenk. 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 Peter Lenk. The network helps show where Peter Lenk may publish in the future.

Co-authors

The 25 scholars most cited alongside Peter Lenk, 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 Peter Lenk Line = papers co-authored together Peter Lenk links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1996391
2 1994153
3 2000127
4 2009125
5 2010103
6 199095
7 199989
8 198877
9 199567
10 200561
11 200256
12 199147
13 199945
14 199739
15 200839
16 200833
17 202029
18 199126
19
Representing heterogeneity in consumer response models
199726
20 200421

About Peter Lenk

Peter Lenk is a scholar working on Statistics and Probability, Marketing, Economics and Econometrics, Artificial Intelligence and Management Science and Operations Research, having authored 59 papers that have together received 2k indexed citations. Recurring topics across this work include Economic and Environmental Valuation (12 papers), Consumer Market Behavior and Pricing (11 papers), Statistical Methods and Bayesian Inference (11 papers), Bayesian Methods and Mixture Models (8 papers), Gaussian Processes and Bayesian Inference (7 papers), Information Retrieval and Search Behavior (3 papers), Statistical Methods and Inference (2 papers) and Healthcare Policy and Management (2 papers). The work is most often cited by research in Marketing (539 citations), Statistics and Probability (292 citations), Economics and Econometrics (516 citations), General Decision Sciences (48 citations) and Management Science and Operations Research (209 citations). Peter Lenk has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include Wayne S. DeSarbo, Greg M. Allenby, Martin R. Young, Paul E. Green, Michel Wedel, Ambar G. Rao, Robert E. Quinn, Katherine A Lawrence, Rajeev Batra and Timothy J. Gilbride. Their work appears in journals such as Marketing Letters, Journal of Marketing Research, Journal of the American Statistical Association, Journal of Business and Economic Statistics and Management Science.

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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