Marketing Is from Mars,IT Is from Venus: Aligning the Worldviews for Firm Performance

A typical firm is operated by multiple functional managers who may collaborate as well as compete to achieve firm performance. In the digital age, firm performance is essentially customer‐dependent and technology‐dependent, with both marketing and information technology (IT) playing key roles. Unfortunately the two functions often have very different worldviews. We show how these differences can damage firm performance, and suggest ways to mitigate this damage. We build a worldview difference model, synthesized from multiple disciplines. The model is tested using both matched and nonmatched observations from marketing and IT managers, and is analyzed with hierarchical linear models using both perceptual and objective firm performance data over a 4‐year period. We find that differences between the beliefs and perceptions of marketing managers and IT managers generate a negative impact on firm performance, and suggest appropriate technology‐culture associations to effectively align their worldviews for firm performance. To improve firm performance, a cross‐functional appreciation for market and technology drivers can be achieved by making marketing managers more learning‐oriented and by providing IT managers a culture that is congruent with technology.     

Cross-country risk quantification of extreme wildfires in Mediterranean Europe

We estimate the country-level risk of extreme wildfires defined by burned area (BA) for Mediterranean Europe and carry out a cross-country comparison. To this end, we avail of the European Forest Fire Information System (EFFIS) geospatial data from 2006 to 2019 to perform an extreme value analysis. More specifically, we apply a point process characterization of wildfire extremes using maximum likelihood estimation. By modeling covariates, we also evaluate potential trends and correlations with commonly known factors that drive or affect wildfire occurrence, such as the Fire Weather Index as a proxy for meteorological conditions, population density, land cover type, and seasonality. We find that the highest risk of extreme wildfires is in Portugal (PT), followed by Greece (GR), Spain (ES), and Italy (IT) with a 10-year BA return level of 50'338 ha, 33'242 ha, 25'165 ha, and 8'966 ha, respectively. Coupling our results with existing estimates of the monetary impact of large wildfires suggests expected losses of 162–439 million € (PT), 81–219 million € (ES), 41–290 million € (GR), and 18–78 million € (IT) for such 10-year return period events.

SUMMARY

We model the risk of extreme wildfires for Italy, Greece, Portugal, and Spain in form of burned area return levels, compare them, and estimate expected losses.     

How to Code a Million Missions: Developing Bespoke Nonprofit Activity Codes Using Machine Learning Algorithms

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - National Taxonomy of Exempt Entities (NTEE) codes have become the primary classifier of nonprofit missions since they were...     

Supporting Quick Response Through Scheduling of Make-to-Stock Production/Inventory Systems*

Scheduling of traditional job shops in make-to-order systems has seen extensive research over the past three decades. In such systems, performance is often related to various job completion metrics such as average flow time, average lateness, etc. This paper examines a scheduling problem in a make-to-stock environment where individual job completion measures are irrelevant. In this case, customer orders are satisfied through on-hand inventory where customer service is more closely related to the manufacturer's ability to quickly satisfy demand. We consider the role of scheduling in reducing inventories and improving customer service in the context of a manufacturer who assembles several different products on a single assembly line. We develop scheduling rules for such a system and experimentally compare their performance to those typically used in such environments. Our results indicate that rules which consider the inventory position and demand forecast outperform traditional fixed cycle rules.