Analysing the impact of a business intelligence system and new conceptualizations of system use

Abstract

Purpose In this study, three models were empirically compared, the DeLone and McLean model, the Seddon model and the Modified Seddon model, by measuring the impact of a business intelligence system (BIS) in companies in Peru. After that, the mediators and dependent constructs were analysed to determine if they were behaving properly (a good level of variance explanation and significant relations with others constructs). The study used a sample of 104 users of the BIS, from companies in several important economic sectors, in a quasi-voluntary context and with six constructs: information quality, system quality, service quality, system dependence (system use), user satisfaction and perceived usefulness (individual impact). Design/methodology/approach To interpret the results, the authors used structural equations. The idea was to look for the best fit and explanations for the outcomes. The main difference in these models is that the DeLone and McLean model considers system dependence (system use) as a part of information system success, but in the Seddon model, it is a consequence of it. Findings The Seddon model seems to show the best fit and explanation for the outcomes. After that, a review of the system use construct was realised, because of its limited variance explained and the few significant relations with other constructs, to improve its explanation power in future research. Research limitations/implications It is estimated that the sample includes more than 15 per cent of all the companies that use a BISs in Peru, so the size of the sample is adequate, but it is not entirely random and therefore limits the generalizability of outcomes. Besides that, a sample size that is bigger could be better for the sake of making a more detailed analysis, permitting the use of some items with less power, or the use of another statistical procedure for structural equations such as the Asymptotical Distribution Free, permitting a more detailed analysis (Hair et al., 2006). Originality/value Business intelligence (BI), one of the most important components of information systems (IS), is playing a very relevant role in business in this time of high competition, high amounts of data and new technology. Currently, companies feel pressured to respond quickly to change and complicated conditions in the market, needing to make the correct tactical, operational and strategic decisions (Chugh and Grandhi, 2013). BI is one of the most important drivers of the decade (Gartner, 2013). Big companies of IS are creating special units specialised in BI, helping companies become more efficient and effective in daily operations.

Design of Question Answering Interactive Open Platform for Power Grid Business Acceptance Robot

“…It is necessary to test the service acceptance accuracy, AUC curve, and long-tail mining ability of the research method of the construction method of the question-and-answer interactive open platform of the power grid service acceptance robot, the method in reference [5], and the reference method [6] to verify the overall effectiveness of the proposed method. e experimental environment is shown in Table 1.…”

“…e higher the accuracy of the predicted position in the document, the higher the recall rate, so the use of P@N the formula for evaluating the question and answer interactive open platform is P@N � |R N |/N, where |R N | represents the number of documents related to questions in the N page documents. According to Figure 9, three methods are used to calculate its similarity and retrieve five different questions, and the first five pages of the results are extracted P@5. e values tend to be close to 1, while the retrieval results of reference [4] and reference [5] are unstable; especially, the retrieval results of reference [5] are only 0.4 in difficult problems. e reason why the proposed method can retrieve any difficult problem is that the user problem is transformed into a vector of spatial dimension by using the vector space model, which makes it easier to calculate its similarity and retrieve the answer, to improve the accuracy of business acceptance.…”

“…As shown in Figure 11, it can be seen that the number of long-tail mining is the largest in the period of 5-6 words, and the long-tail mining ability continues to decline when the number of keyword words exceeds more than 10. However, the minimum number of relevant answers mined by the proposed method is 15, while the maximum number of relevant answers in document [4] is only 14, and the number in document [5] is less than 11, because the proposed method converts all documents into feature vectors with dimensions, sets weights for all words in the document, places them in the coordinate system, and normalizes the words. erefore, the answers to questions can be matched more comprehensively, and the number of answers that can be matched by keywords also increases, which improves the ability of mining long tails.…”

“…Among them, research and development of the power grid business acceptance robot have a signi cant impact on power grid business acceptance e ciency [2,3], but this method has the drawback that the power grid business acceptance robot's response is inconsistent with the needs of customers, which not only slows down business ow but also increases the workload of power grid business acceptance [4]. Reference [5] empirically compares three models by measuring the impact of powerful business intelligence systems (BIS) on Peruvian companies, namely the Delone and McLean model, the Seddon model, and the modi ed Seddon model. en, the mediation and dependency structure is analyzed to determine whether their behavior is normal.…”

Business Intelligence Tools for a Digital Services Company in Peru, 2022

“…For their part, Gonzales and Wareham (2018) assessed the impact of Business Intelligence on companies based on the DeLonge and McLean and the Seddon and Seddon models. 104 companies using Business Intelligence systems (BIS) were selected, and six dimensions were used as a reference: system quality, service quality, information quality, system dependency (system use), user satisfaction, and perception.…”