Effects of firm and IT characteristics on the value of e-commerce initiatives: An inductive theoretical framework

Andoh‐Baidoo, Francis Kofi; Osei-Bryson, Kweku-Muata; Amoako‐Gyampah, Kwasi

doi:10.1007/s10796-010-9234-4

Cited by 18 publications

(10 citation statements)

References 55 publications

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“…This argument is supported by Humprey, Snyder and Willis (Humphrey et al 1991), who state that risk and uncertainty are reduced, as BPM maturity increases. Along the same lines, various studies show relations between aspects of company culture and levels in which certain aspects of a company are structured (Ali and Green 2012;Andoh-Baidoo et al 2012). Consequently, we would expect that there is a positive relation between the uncertainty avoidance index of the country of an organization and the BPM maturity level.…”

Section: Research Modelmentioning

confidence: 82%

Properties that influence business process management maturity and its effect on organizational performance

2015

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BPM maturity is a measure to evaluate how professionally an organization manages its business processes. Previous research provides evidence that higher BPM maturity leads to better performance of processes and of the organization as a whole. It also claims that different organizations should strive for different levels of maturity, depending on their properties. This paper presents an empirical investigation of these claims, based on a sample of 120 organizations and looking at a selection of organizational properties. Our results reveal that higher BPM maturity contributes to better performance, but only up to a point. Interestingly, it contradicts the popular belief that higher innovativeness is associated with lower BPM maturity, rather showing that higher innovativeness is associated with higher BPM maturity. In addition, the paper shows that companies in different regions have a different level of BPM maturity. These findings can be used as a benchmark and a motivation for organizations to increase their BPM maturity.

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Section: Research Modelmentioning

confidence: 82%

Properties that influence business process management maturity and its effect on organizational performance

2015

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“…Big data, however, can be viewed as an innovative information system, such as big data storage capacity and grid technologies (Garlasu et al, 2013) and cloud computing (Purcell, 2014). In this sense, the value of big data investments tends to be intangible and emerges over the long term, which can hardly be reflected on the balance sheet (Andoh-Baidoo et al, 2012). Investments in big data can also involve acquiring big data experts (e.g.…”

Section: Measuring the Value Of Big Datamentioning

confidence: 99%

“…Prior studies in the information systems field have proposed a number of possible measures to assess the economic value of information system investments. Some example measures include accounting based measure such as return on investment, return on assets and return on equity (Schryen, 2013), Tobin's Q (Bharadwaj et al, 1999;Bardhan et al, 2013) and stock performance (Im et al, 2001;Andoh-Baidoo et al, 2012;Roztocki and Weistroffer, 2015). Among these measures, stock performance is likely to be appropriate for this study for the following three reasons.…”

Section: Measuring the Value Of Big Datamentioning

confidence: 99%

“…Hence, it is difficult to use common accounting measures to cover the hidden value of big data investments, such as cost reduction and improvement in products, services and decision-making (Huang et al, 2016). Stock performance can reflect the overall trust of market investors in the ability of the firm to derive benefits from information system investments (Im et al, 2001;Andoh-Baidoo et al, 2012;Son et al, 2014;Roztocki and Weistroffer, 2015).…”

Section: Measuring the Value Of Big Datamentioning

confidence: 99%

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Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

Zhang

Wang

Pauleen

2017

JKM

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Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

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“…For instance, DT induction has been employed to develop theoretical models for examining ERP systems and end-user performance (e.g., Delen et al, 2009;Kositanurit et al, 2011). Further, DT induction has been used to examine investors' response to internet security breach (Andoh-Baidoo, 2013; and Electronic Commerce (Andoh-Baidoo et al, 2012 announcements. In this paper, we use DT to profile IB full adopters, partial adopters, and non-adopters, and identify the most important predictive factors of IB adoption.…”

Section: Introductionmentioning

confidence: 99%

An exploratory analysis of internet banking adoption using decision tree induction

Takieddine¹,

Andoh‐Baidoo²

2014

IJEF

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Abstract:Although internet banking promises several benefits to bank customers, the adoption of online banking services continue to be low especially in developing countries. Prior research has examined internet banking adoption as a classification problem with a focus on adopters and non-adopters using traditional statistical approaches. In this paper we extend the problem by including partial adopters and used decision tree induction as the analytical method. Our results on data from customers in 20 countries show that household income is the most important variable in separating those who do not adopt IB or partially adopt IB from those who fully do. Education and age are the most important variables in separating partial adopters from non-adopters. While exploratory, our results provide opportunities for further theory development on internet banking adoption while practically contributing to discussions on how practitioners can develop specific strategies to targeted customers to become fully adopters of online banking services.

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Effects of firm and IT characteristics on the value of e-commerce initiatives: An inductive theoretical framework

Cited by 18 publications

References 55 publications

Properties that influence business process management maturity and its effect on organizational performance

Properties that influence business process management maturity and its effect on organizational performance

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

An exploratory analysis of internet banking adoption using decision tree induction

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