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Leeflang and Wittink (2000) divided the models for marketing to five areas. The first era focuses on the application of existing operation research and management science; the second era concerns the use of models to fix marketing problems; in the third era, models put emphasis on representing reality and ease of use; in the fourth era, there is an increase in routinized models and studies of generality of results; in the fifth era, information dominates the marketing processes. Current studies are still at this stage.
Therefore, reviews for approaches to model information based activities concentrate on fundamental models in the fourth and fifth areas. Key models based on information flows are discussed in this section.
The input-process-output (IPO) model is proposed by King (1988). He builds the model for planning information systems in an organisation. This model treats information as both input, or resources from the environment,and output, that could have some effects to the organisation’s performance.
In one IPO model, the direction of input and output is also the direction of the value chain. Chaffey (2007) introduces a revised IPO model in an IIO as shown in Figure 2.1. This IPO model begins from the stage of market research to find market demand and requirements from potential customers;develop the new product by designing, prototyping and testing; market the products using prototypes (e.g. demonstration versions of the products);organize materials for large-scale production, most probably based on the orders received; finally, sell the products and provide after-sale services.The first two stages in the IPO model ensure that the new products are linked closely to the market demand. Information in this IPO model helps a business to operate on a just-in-time (JIT) manner, with little cost for inventory.
Figure 2.1 Revised IPO Model in IIO (Chaffey, 2007, pp12 )
Keil (2001) developed the IPO in IIOs based on Chaffey’s model, and separated the information flow from the delivery flow. As shown in Figure 2.2,this model concentrates on the demand and feedback from customers in the market. IIO produces and provides goods and services based on the need of the customers. Therefore, the overall organisation could constantly adjust itself to suit changes in a dynamic market.
Figure 2.2 Keil’s IPO Model (Keil, 2001)
Keil’s IPO model is possible only when information is treated as an intrinsic part of the business. Information is an indispensable factor along with labour, capital, physical resources and entrepreneurs. Business can only survive based on providing goods or services that customers need. Correct information from customers to judge what there need is critical. It emphasises the importance of information in a real-time environment.
Figure 2.3 illustrates an IPO model based on e-business as proposed by Liu and Gu (2010). Generally, the IIO can be divided into three parts, the suppliers or partners, organisation and customers. The main function of an IIO is to integrate information both from suppliers and customers, process it and then provide information that adds value. Unlike a traditional business,e-business IIOs operate on networks that include activities such as working closely with suppliers and partners, creating click-and-mortar virtual distributed networks, and delivering products and services to customers through networks. In this model, the value is added both from suppliers and customers. The IPO model enables the enterprise to gather and analyse the information from the market efficiently and improve the speed and quality of decision-making which will strengthen the enterprises’ competitive advantage.
Figure 2.3 Keil’s IPO Model (Keil, 2001)
The IPO model defines general information flow directions among suppliers, IIOs and customers. It reflects the value added with information flows in IIOs and clearly describes the overall structure of a business.However, Trainer (2004) and Eseryel (2002) also point out the weaknesses of the IPO model, including: (1) the lack of necessary steps to use by practitioners when applying the model; (2) inability to identify the dynamic interaction in an detailed level of information flows in the organisation; (3) the lack of a method to directly assess business performance.
Due to the weaknesses of the IPO model, methods to describe and analyse specific information flows are proposed. One of the main schools of thought in modelling IIOs is the data process model. The IPO model shows the general direction of information flows and builds up the general structure of a business, and data process modelling methods describe the specific information flows in the general structure.
The data process model describes organisational behaviour from an information flow perspective, from the beginning to the end. Marketing science is a process (Bass, 1995). In each information flow, data constructs information and streams along with the flow. Data was processed to valuable ones and provided to the receivers. The positive effect of marketing can be guaranteed by effective management over the control of marketing activities.Process models demonstrate data flows among customers, IIOs and suppliers.This approach includes the structural model for measuring customer experience (Novak et al., 2000), circular market model for direct marketer and multiple retailers (Balasubramanian, 1998), brand choice model for online and offline transactions (Degeratu et al., 2000), and duopoly model for online and conventional channels (Zettelmeyer, 2000) and many others. The structural model for measuring customer experiences is constructed based on a web-based business. It describes the flows of information in the process of purchasing decisions. The model is developed from the concept of data flow diagram (DFD), with description of data flows and direction of the flows. The circular market model for direct marketer and multiple retailers studies the difference between online marketing and traditional marketing. The information flow from sellers can be better controlled by traditional marketing methods but not the internet ones, as the comments and feedback of the products from previous customers are available along with description of the products. The model uses the concept of storage for data in DFD to discuss the coverage and level of information in direct marketing. The brand choice model for online and offline transactions studies the function of brand names in online and offline market. Online customers are more sensible of the names of brands. Brands names which can reflect the function of the products are more valuable in online business. The model examines the customers’purchasing behaviours from the perspective of information acquiring. A description of data flows and direction of the flows in DFD are used in the brand choice model. The duopoly model for online and conventional channels discusses how the pricing and communication strategies are designed and applied in both online and offline channels. The model builds a mechanism with both channels and the two parts behave as drivers to each other.Information for communication flows between the two parts and integrates the two components as a whole process to facilitate consumers search for their products. The concept of data flows in DFD is used again in this model.
Based on the above review and discussion, the data flow diagram (DFD) is an essential method to model these data flows in a business. DFD is a widely used method that shows the relationships among agents within an organisation. It is based on the Structured Systems Analysis and Design Methodology for Designing Information Systems (SSADM) (Skidmore,1999). It shows how information enters and leaves the process. DFD also analyses the activities which can change the information and where the information is stored within the process. DFD could describe IIOs straight forwardly, and make explicit the information flow within the organisation.
Figure 2.4 is an example of a DFD method to describe the data flows in an IIO based on the IPO model. DFD has four main components, processes,data flow, data stores and external entities (Holstein and Seagle, 1987). DFD could be developed further to specific tools to model a business, such as workflow diagrams, activity diagrams, use case diagrams, functional decomposition diagrams. All these diagrams are focused on business processes (Wieringa, 2003).
The main strength of DFD is that the detailed data flow is very clear(Sorensen and Fountas, 2009). Arrows in the diagram reflect the direction of data flow and also identifies the sender and receiver of each route. This method could answer the questions of who, what, where and when according to the previously identified criteria. However, the main problem of this method is its inability identify the function and effect of information. In other words, it cannot answer the question as to why a particular person or department in the organization needs a particular piece of information (Kock, 2009). Additionally, DFD requires the modeller to have both a general and specific view of the business process (Turetken, 2007). Data flows in an organization are complex requiring a relatively long time to understand and model. The method of DFD concentrates more on analysing specific data flows, which construct the business operation. However, in business modelling, functions of different parts in an organisation need to be understood. DFD is short for a consideration for the general picture of a business. Some communication pathways can be missed.
Figure 2.4 The Revised IPO Model (Liu and Gu, 2010)
To address the weakness of DFD method and help the business managers to have a general view on the function of information, the methods for functional modelling were developed. As argued by Leeflang and Wittink(2000), in the fifth era of market modelling, information plays a dominant role. The function and effect of information are widely discussed and many models are proposed by researchers to analyse the intrinsic content of information, such as the models using mathematics and social network analysis.
Mathematical model metrics aim to identify the relationship between parameters to analyse the effect of information in marketing. The Bayesian model (Ansari et al., 2000) and Hierarchical Bayes methods (Lenk et al.,1996) establish parameters in analysing feedback from customers. The mathematical statistical approach (Bakos and Brynjolfsson, 2000) discusses the optimal form of providing goods over the Internet. The stochastic model of individual behaviour (Moe and Fader, 2004) describes consumer behaviour in online information seeking and decision-making. The structural equation model (Gasmi et al., 1992; Pearl, 2000) identifies latent and missing variables in an analysis which are quite suitable to the unpredictable and dynamic features of marketing activities (Steenkamp and Baumgartner, 2000).
The main advantages of mathematical modelling metrics are that they are logical, abstract, and clear. They are useful especially in modelling complex organisations. These models can be used in depth to analyse functions and effects of information flows. The key problem is the mathematical modelling metrics is difficult in creating the model as not everything can be captured mathematically. Moreover, analysts may face difficulties in understanding and applying mathematical formulas and the meanings they represent.
Social Network Analysis (SNA) studies the pattern of relations among people, departments, and organisations (Anderson, 2002). It focuses on the social aspects of the organisation, such as the social structure, social position,and social ties (Chung and Hossain, 2010). It assesses information opportunities for individual or groups to explore and control information(Haythornthwaite, 1996). Basically, there are four components in this method,including unit (node), relations among the units (links), properties of the relation, and the level of analysis (Scott, 1991). The nodes represent different people or departments in an organisation while the links stand for their relations. Various levels of communication and small groups with closed relations could be identified by this method and strong ties. Weak ties and structural holes are the key areas which are identified for improvement(Granovetter, 1973; Granovetter, 1974; Granovetter, 1983).
By applying SNA to model information flow, formal and informal communication——which is influenced by culture——can be identified(Hossain and Oboukhova, 2009). Likewise, SNA could help to identify levels of communication and assess overall levels of inter-groups coordination(Hagen et al., 1997). Thus, SNA can reflect the pattern of information flows among people or organisations thus describing the level of relations among these stakeholders.
Although SNA can model the specific information flows and reflect the function of them in IIO, it has its own weaknesses. SNA focuses on analysing relations among stakeholders rather than activities (Webster and Morrison,2004; Hjorland, 2013). Another problem is that SNA is unable to test hypotheses statistically (Scott, 2012). The data for SNA is auto correlated and cannot be applied to classical statistical tests. Therefore, the value of each information flow cannot be assessed by this method.
Bosse (2004) proposed the biological model which demonstrates the function of information as human organics. The model borrows the biological system of mammals and models the complex information system in business.The whole business is organised by connecting each functional organics which are self-operated and interlinked. Business activities behave as the nerves and blood to transfer information to each functional department.Besides, there are many other methods discuss the function and effect of information from various perspectives, such as the extension model of DEMO from a semiotic perspective by Liu et al. (2003), MITAIS by Sun (2001), Fractal Model of Communication by Baranauskas (2002), etc. The essential parts of these methods are based on the IPO model, process modelling methods, and functional modelling methods reviewed above.
As seen above, approaches for modelling information based activities in an IIO can be grouped to three categories, IPO model, process modelling methods, and the functional modelling methods. IPO model gives a general direction of information based activities movement in an organisation, while it cannot describe specific activities. Process modelling models describe the activities in detail but cannot answer why the information is needed and may miss some information flows. Functional modelling methods can demonstrate the effects of the marketing activities in IIOs, but it cannot describe the specific activities in the organisation.
Thus, IPO and process models can be partly used to model information based marketing activities in IIOs, but need to be further developed.Information based marketing activities need to be identified comprehensively by the developed methods, and are the bases for business performance measurement in the next step. Functional modelling methods reviewed can reflect the function of information, but generally they are not effective to be applied in practical use. An effective method is needed to demonstrate the functions of information based activities. Meanwhile, “function” or the effects of researched activities, can be assessed by the business performance measurements directly.