The B2B Knowledge Gap
By Gary L. Lilien. 2016
328 citations recorded by the source corpus.
Subfields: Computer Science Applications, Management Information Systems, Strategy and Management
View Paper: https://www.semanticscholar.org/paper/a7ba1c26cf2cf3fd38e4f5bf66422680eb873194
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
- Marko Robnik-Sikonja
- Marko Bohanec
- Jonas Schmidt
- Shivam Gupta
- Mirjana Kljajic Borstnar
- Tammo H. A. Bijmolt
In Management Information Systems
- Marko Robnik-Sikonja
- Marko Bohanec
- Surajit Bag
- Ajay Kumar
- Jonas Schmidt
- Shivam Gupta
- Mirjana Kljajic Borstnar
- Uthayasankar Sivarajah
In Strategy and Management
- Marko Robnik-Sikonja
- Marko Bohanec
- Surajit Bag
- Ajay Kumar
- Jonas Schmidt
- Shivam Gupta
- Mirjana Kljajic Borstnar
- Uthayasankar Sivarajah
Citations in the corpus
- When and why do customer solutions pay off in business markets? (2017, unknown): “2015), specifically whether and when solutions offerings pay off (Lilien 2016).”
- Networks, ecosystems, fields, market systems? Making sense of the business environment (2020, unknown): “To make marketing as a discipline and business marketing in particular relevant again, we must re-train our research efforts on the complexities of the value-creating context, aiming for managerially relevant research results (Jaworski, 2011; Lilien, 2016; Reibstein et al., 2009).”
- An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance (2021, background): “Lilien (2016) highlighted the existence of a B2B knowledge gap and suggested stronger academic research output to fill the gap and enable knowledge creation.”
- Explaining machine learning models in sales predictions (2017, methodology): “…can be used (Meyer et al., 2014; Florez-Lopez and Ramon-Jeronimo, 2015), introducing a real-world application of ML models to B2B practitioners (Lilien, 2016), allowing reduction of personal bias in sales forecasting (Armstrong et al., 2015), moving beyond the win-propensity score (Yan et al.,…”
- Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias (2019, background): “Therefore, we join Lilien (2016) in calling for more research in B2B marketing, including the measurement of WTP.”