Forecast competitor service strategy with service taxonomy and CI data

Abstract

Purpose -The paper aims to develop a service taxonomy model and a mathematical process to forecast a competitor's service business strategy in a multiple service business context by inputting CI data such as the profits of existing core services. Design/methodology/approach -A qualitative method of literature review is adopted to build a service taxonomy model and form two propositions. Based on the multiple business process integration concept of the resource-based view, a mathematical process constituted by service modules and weights is developed. Salient components of the competitor's service business are identified to forecast the competitor's service business strategy after utilizing optimization heuristics of 80/20 and large number rules. Findings -The model is able not only to forecast a competitor's service business strategy, but can also help develop the firm's own new service strategy. The resources of the firm can then be realigned to counteract the competitor's strategy.Research limitations/implications -The developed model is mainly applicable to service businesses. The collection and use of CI data must consider ethical issues, which might limit the sources of data. Practical implications -To forecast a competitor's strategy correctly , good quality CI practice is necessary. Experienced people in the CI department are critical to the production of good quality forecasts. Originality/value -The contribution to CI impact studies is that the mathematical forecasting process is developed based on qualitative service taxonomical research. Key elements of the service process are identified as salient elements, which serve as the main focusing points in forecasting competitors' strategy.

35 years of research on business intelligence process: a synthesis of a fragmented literature

“…Conversely, literature with scaffolding in information systems and decision support, fueled by the desire of bridging the gap between the business user and BI transformation and usage, criticized the firms’ focus on collection over analysis despite the challenge of information overload and gave significant attention to testing in-house acquisition techniques of BI collection to curb the exorbitant price of third-party sources by proposing Limited Information NBD/Dirichlet (LIND) models to infer key competitive measures based on site-centric data (Zheng et al , 2012) or two level conditional random fields (CRF) models to extract comparative relation features from entities and words (Xu et al , 2011) or event detection (NEED) applications that perform events detection based on properties extracted from news stories (Wei and Lee, 2004) or proposed 80/20 rule-based models for reduction of cycle time (Kohavi et al , 2002; Liu and Wang, 2008) or suggested data slicing and dicing technologies, which index and analyze documents collected from websites matching users’ interest (Chen et al , 2002) or grant rapid access displays of data (Walters et al , 2003). One commonality within this research stream is the evaluation of the proposed tool against the commercial engines (Chen et al , 2002; Zheng et al , 2012; Xu et al , 2011).…”

Of BI research: a tale of two communities

“…The common theme across publications in the CI cluster (Quadrant 3, Figure 1) is the use of eclectic definitions of intelligence concepts that fall into two research streams: CI as a product and CI as a process. The former regards CI as the final intelligence or knowledge delivered to the business user (Chen et al, 2002;Xu et al, 2011;Zheng et al, 2012); the latter considers it a sequential activity through which it funnels intelligence to support organizational objectives (Dishman and Calof, 2008;Liu and Wang, 2008;Wright et al, 2009) and whose budgeting enhances organizational vigilance against environment uncertainty (Opait et al, 2016).…”

Revisit service classification to construct a customer‐oriented integrative service model

“…For the purpose of building a mathematical model for service business analysis, we adopted the 3P + C (provider, process, place, and customer) service classifying model proposed by Liu and Wang (2008). Liu and Wang (2008) classify the structures of the previous classifications of services based on the schemes concepts, i.e. discrete item scheme, continuum scheme, and matrix scheme.…”

“…Liu and Wang (2008) have analyzed the advantages and disadvantages of the three schemes but without listing the related literatures of them. Main researchers of the service classifications in continuum and matrix schemes are listed in Tables I and II, respectively.…”