You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Applications of belief functions in business decisions: A review 
Chapter Title:  Business Model Combination 
1. Abstract of Cited Paper (BIB001):  Studies indicate that mergers and acquisitions are characterized by a high failure rate, often attributed to an inability on the part of the acquiring firm management to effectively evaluate potential acquisition candidates. This is not surprising given the considerable uncertainties surrounding both the relationships among factors in the evaluation process and also in the assessment of evidence. This paper develops a conceptual framework for acquisition and merger decisions using evidential reasoning approach under the belief function framework. It seeks to illustrate how expert knowledge of relevant factors can be mapped and how an evidential network can be used by decision makers to incorporate uncertainties in the evidence. Also highlighted is the fact that the nature and extent of evidence that needs to be collected depends on the rules assumed to govern relationships among input variables. Implications for theory and practice are discussed. 
2. Abstract of Cited Paper (BIB002):  The main goal of this paper is to demonstrate how the theory of belief functions [Dempster 1967, Shafer 1976, Kohlas and Monney 1995, Smets 1998] can be used to model financial portfolios. In particular, we are interested in modeling how a portfolio return distribution changes as we learn new information about the different factors that impact the portfolio.

Literature Review:
Many applications of DST deal with how to combine business data and models from multiple sources. Such applications include business forecasting, portfolio management, information quality assessment, etc. Unlike the applications in decision-making under ambiguity, these applications typically utilize the other advantages of the belief function modeling such as the combination of independent evidence by Dempster's rule and the representation of complex uncertainty by graphical models. The AICPA and Canadian Institute of Chartered Accountants jointly published the criteria and principles to provide WebTrust services for creating trust in consumers' minds to conduct business through the Internet (AICPA 1997b (AICPA , 1997c . Based on those criteria and principles, BIB001 propose an evidential network for providing assurance on WebTrust services using belief functions.  use a real case of a WebTrust service and determine the structure of the evidential network in the case. They have performed a sensitivity analysis to determine the relative importance of the evidence at various levels of the network and also investigated the impact of 'weighted average' relationship among the WebTrust sub-objectives and the main objective instead of 'and' relationship on the overall assurance or belief that the main objective is met. It is important to note that the 'weighted-average' relationship appears to map the auditor's judgment better than the 'and' relationship. Similarly,  propose an evidential network for determining the quality of information. They identify the main attributes that determine the information quality. They argue that logically, similar to audit objectives, the information quality is high if and only if all the attributes are true. In other words, all the attributes are related to the main objective, the information quality being high, through an "and" relationship. Other relationships such as 'weighted average' with equal weights and varying weights are being investigated. BIB002 apply the belief functions approach to the evaluation of mergers and acquisitions candidates. They also develop an evidential network for the decision and use belief functions to represent uncertainty in the evidence and use Dempster's rule of combination to determine the overall belief whether the target company is a good candidate of merger and/or acquisition. McBurney and Parsons (2002) apply the belief-function framework to forecast demand for mobile satellite services. They find that belief functions provide a means of representing and combining varied beliefs for forecasting demand, which is more expressive than traditional point probability estimates. In a similar application, Bryson (2002) applies Dempster's rule to combine subjective probability judgments from a group of experts. He presents an integrated structured, non-invasive action learning knowledge elicitation process for eliciting from a group of experts, the subjective probabilities that are required by some DST-based expert systems. This process accommodates the expert's uncertainty, identifies inconsistencies in the expert's opinion, takes advantage of the potential benefits of group work while mitigating against the negative effects, and addresses the issues of consensus assessment and consensus building. BIB003