Shameek Sinha

@article{sinha_1363,

title = {On Ethereal Grounds: Cultural Resources as Foundations Supporting Innovation Success},

author = {Shameek Sinha and Ahmad Hijazi},

url = {https://www.tandfonline.com/doi/abs/10.1080/08961530.2020.1798837?journalCode=wicm20},

year  = {2021},

date = {2021-08-01},

journal = {Journal of International Consumer Marketing},

volume = {33},

number = {4},

pages = {399-417},

abstract = {This paper examines impacts that cultural resources can have on the success of innovation-promotion efforts. We explore how cultural structures can imbue messages with different cognitive, affective, and behavioral qualities, bridging gaps resulting from uncertainty and ambiguity, and improving the chances of innovation success. The study uses a web-administered experiment that tracks responses to an innovation-promoting ad, showing how varying degrees of innovativeness are associated with varying levels of culturally-caused impacts, and exploring mediating variables. We examine how increasingly innovative (more complex, more ambiguous) products, associated with lower perceived informativeness, gain better cognitive, affective, and behavioral consumer responses due to their association with cultural resources, belonging to a favorable ?Cultural Resource Set.?},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mcalister_1402,

title = {A Customer Portfolio Management Model that Relates Company's Marketing to its Long-term Survival},

author = {Leigh McAlister and Shameek Sinha},

url = {https://link.springer.com/article/10.1007%2Fs11747-020-00765-9},

year  = {2021},

date = {2021-05-01},

journal = {Journal Of The Academy Of Marketing Science},

volume = {49},

number = {4},

pages = {584-600},

abstract = {A typical customer relationship management model is designed to increase the value of a company's existing customers in the next period. While useful in the short term, such a process, followed blindly period after period, would drive the company out of business when those existing customers all eventually died. In reality, no company would do this. Instead, these short-term models are nested within a long-term view of customer management, and it is long-term customer management that the proposed model addresses. The model assumes that a company has identified a set of customer types across which it needs balance in order to remain viable in the long-term (e.g., a company might wish to maintain a supply of ?entry-level customers? in order to eventually replenish its collection of more profitable ?loyal customers?). Though the model is applicable in any industry, we illustrate it for automobiles. Results reveal the strengths with which each marketing intervention causes General Motors to attract each of their desired customer types. The model is extended to also reveal differences in the ways that marketing interventions by Ford, Toyota, and Honda change the strengths with which those automakers attract customers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{duan_1516,

title = {Re-Examining Bayesian-Model-Comparison Evidence of Cross-Brand Pass-Through},

author = {Jason A. Duan and Leigh McAlister and Shameek Sinha},

url = {https://pubsonline.informs.org/doi/abs/10.1287/mksc.1100.0611},

year  = {2011},

date = {2011-06-01},

journal = {Marketing Science},

volume = {30},

number = {3},

pages = {550-561},

abstract = {Using the Bayes factor estimated by harmonic mean [Newton, M. A., A. E. Raftery. 1994. Approximate Bayesian inference by the weighted likelihood bootstrap. J. Roy. Statist. Soc. Ser. B.56 (1) 3-48] to compare models with and without cross-brand pass-through, Dubé and Gupta [Dubé, J.-P., S. Gupta. 2008. Cross-brand pass-through in supermarket pricing. Marketing Sci.27 (3) 324-333] found that, in the refrigerated orange juice category, a model with cross-brand pass-through was selected 68% of the time. However, Lenk [Lenk, P. J. 2009. Simulation pseudo-bias correction to the harmonic mean estimator of integrated likelihoods. J. Comput. Graph. Statist.18(1) 941-960] has demonstrated that the infinite variance harmonic mean estimator often exhibits simulation pseudo-bias in favor of more complex models. We replicate the results of Dubé and Gupta in the refrigerated orange juice category and then show that any of three more stable finite variance estimators select the model with cross-brand pass-through less than 1% of the time. Relaxing the assumption that model errors are distributed normally eliminates all instances in which the cross-brand pass-through model is selected. In 10 additional categories, the harmonic-mean-estimated Bayes factor selects the model with cross-brand pass-through 69% of the time, whereas a finite variance estimator of the Bayes factor selects the model with cross-brand pass-through only 5% of the time. Applying arguments in McAlister [McAlister, L. 2007. Cross-brand pass-through: Fact or artifact? Marketing Sci.26 (6) 876--898], these 5% of cases can be attributed to capitalization on chance. We conclude that Dubé and Gupta should not be interpreted as providing evidence of cross-brand pass-through.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}