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This study investigates the influence of Marketing Intelligence on Business Competitive Advantage: A study of Diamond Bank Plc, Nigeria. Five objectives were identified and translated into five research questions which aptly answered by subjecting them to a test of hypotheses. A Descriptive research was used to survey 292 members of the staff of Diamond Bank in Lagos, Central Regional branches and head office. Out of which 285 responses were obtained, while 6 responses were destroyed because they were not properly filled. Also, data was collected through a self-administered questionnaire, as Pearson correlation, T-test and Regression were used to test the hypotheses statements. However, the result of the findings revealed that marketing intelligence sub-constructs such as internal records, competitor’s sales data, marketplace opportunity, competitors’ threats and competitors’ risks have significant and positive influence on business competitive advantage. Thus, it can be concluded that marketing intelligence as sensitive information has enabled the bank to successfully acquire more profit, expand the branch network all over the country, perform better than its rivals in the market and increase its business competitive advantage.

Research Summary Is moderate distinctiveness optimal for performance? Answers to this question have been mixed, with both inverted U and U‐shaped relationships being argued for and found in the literature. I show how nearly identical mechanisms driving the distinctiveness‐performance relationship can yield both U‐shaped and inverted U‐shaped effects due to differences in relative strength, rather than their countervailing nature. Incorporating distinctiveness heterogeneity, I theorize a U‐shaped effect in homogeneous categories that flattens into an inverted U in heterogeneous categories. Results combining a topic model of 69,188 organizational websites with survey data from 2,279 participants in the Dutch creative industries show a U‐shaped effect in homogeneous categories, flattening and then disappearing in more heterogeneous categories. How distinctiveness affects performance thus depends entirely on how distinct others are. Managerial Summary A core strategy recommendation is to be different from competitors. Recent work highlights the notion of optimal distinctiveness—being different enough to escape competition yet similar enough to be legitimate, thus yielding the highest performance. This article challenges the notion that one “optimal” level of distinctiveness exists and focuses on distinctiveness heterogeneity (representing variation in firm positions in a category) as a key contextual factor. Results from a sample of firms in the Dutch creative industries show that either being entirely different or entirely the same to competitors pays off when one's category is very homogeneous. However, being different loses its performance effects entirely when heterogeneity in firm positions is higher. Being different from competitors, therefore, no longer pays when others tend to be different, too.

We study how firms differ from their competitors using new time-varying measures of product similarity based on text-based analysis of firm 10-K product descriptions. This year-by-year set of product similarity measures allows us to generate a new set of industries in which firms can have their own distinct set of competitors. Our new sets of competitors explain specific discussion of high competition, rivals identified by managers as peer firms, and changes to industry competitors following exogenous industry shocks. We also find evidence that firm R&D and advertising are associated with subsequent differentiation from competitors, consistent with theories of endogenous product differentiation.

Many natural competitors are jointly held by a small set of large institutional investors. In the U.S. airline industry, taking common ownership into account implies increases in market concentration that are 10 times larger than what is \"presumed likely to enhance market power\" by antitrust authorities.¹ Within-route changes in common ownership concentration robustly correlate with route-level changes in ticket prices, even when we only use variation in ownership due to the combination of two large asset managers. We conclude that a hidden social cost—reduced product market competition—accompanies the private benefits of diversification and good governance.

Anthropologists, like neoliberal economists, have often assumed that competition (re)orders society in broadly predictable ways. By contrast, we contend that competition always facilitates changes beyond its anticipated outcomes and disciplinary effects. We argue that the outcomes of competition are contingent on the varied and co-existing interpretations of audiences, arbiters, and competitors about the nature of competition, what is worth competing for, and how to go about it. Hence, although it is often instituted with the intention of authoritatively determining value, generating order, or engineering predefined changes, competition inherently affords alternative and unexpected possibilities for sociality. In doing so, competition mediates divergent social orders and modes of relating, rather than instituting one order or another.

Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a non-linear fashion, such as when the response is binary. Although this modelling problem has been extensively studied, it remains unclear how to control the fraction of false discoveries effectively even in high dimensional logistic regression, not to mention general high dimensional non-linear models. To address such a practical problem, we propose a new framework of ‘model-X’ knockoffs, which reads from a different perspective the knockoff procedure that was originally designed for controlling the false discovery rate in linear models. Whereas the knockoffs procedure is constrained to homoscedastic linear models with n ⩾ p, the key innovation here is that model-X knockoffs provide valid inference from finite samples in settings in which the conditional distribution of the response is arbitrary and completely unknown. Furthermore, this holds no matter the number of covariates. Correct inference in such a broad setting is achieved by constructing knockoff variables probabilistically instead of geometrically. To do this, our approach requires that the covariates are random (independent and identically distributed rows) with a distribution that is known, although we provide preliminary experimental evidence that our procedure is robust to unknown or estimated distributions. To our knowledge, no other procedure solves the controlled variable selection problem in such generality but, in the restricted settings where competitors exist, we demonstrate the superior power of knockoffs through simulations. Finally, we apply our procedure to data from a case–control study of Crohn’s disease in the UK, making twice as many discoveries as the original analysis of the same data.

Consumer reviews are now part of everyday decision making. Yet the credibility of these reviews is fundamentally undermined when businesses commit review fraud, creating fake reviews for themselves or their competitors. We investigate the economic incentives to commit review fraud on the popular review platform Yelp, using two complementary approaches and data sets. We begin by analyzing restaurant reviews that are identified by Yelp’s filtering algorithm as suspicious, or fake—and treat these as a proxy for review fraud (an assumption we provide evidence for). We present four main findings. First, roughly 16% of restaurant reviews on Yelp are filtered. These reviews tend to be more extreme (favorable or unfavorable) than other reviews, and the prevalence of suspicious reviews has grown significantly over time. Second, a restaurant is more likely to commit review fraud when its reputation is weak, i.e., when it has few reviews or it has recently received bad reviews. Third, chain restaurants—which benefit less from Yelp—are also less likely to commit review fraud. Fourth, when restaurants face increased competition, they become more likely to receive unfavorable fake reviews. Using a separate data set, we analyze businesses that were caught soliciting fake reviews through a sting conducted by Yelp. These data support our main results and shed further light on the economic incentives behind a business’s decision to leave fake reviews. This paper was accepted by Lorin Hitt, information systems .