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Article Type: Editorial From: Journal of Enterprise Information Management, Volume 27, Issue 4
It gives us great pleasure to welcome our readers to the fourth issue of the 27th volume of Journal Enterprise Information Management (JEIM), and to express our appreciation for their continuous support during the past year. The continuous update of the journal's scope to promote theory and practice has led to an increase in submissions that has allowed us to further the quality of the journal. This issue incorporates excellent “quality” submissions that focus on providing pragmatic contributions to theory and practice.
The fourth issue of volume 27 commences with a research paper by Arpan Kumar Kar and Ashis Kumar Pani, entitled “How can a group of procurement experts select suppliers? An approach for group decision support”. Effective supply chain management manages supply chain activities to maximise customer value and realise a sustainable competitive advantage, resulting in the overall competitiveness of the organisation (Hassini, 2008). The authors argue that it is vital to optimise imperative activities (e.g. procurement management, supplier selection, etc.) in the supply chain using systemic approaches. According to Kerr and Tindale (2004), intricate processes and the communal intellect of a group of decision makers in agreement is often considered as more effective than the same decision makers functioning in segregation and then “summed” together. The literature review presented in the paper argues that there is limited research focusing on applying theories for group decision making to the supplier selection problem, even though on an individual basis both domains have a plethora of literature. The authors assert that this literature void highlights the need to assimilate the two forms of knowledge (i.e. group decision making and supplier selection problem) to supplement decision support literature in procurement. In order to address this void, the authors use the analytic hierarchy process (AHP) technique (Saaty, 1980) to provide group decision support to the supplier selection problem, thereby, using a case study to test the theories of consensual group decision making when compared with other approaches. The empirical research clearly endorses that Row Geometric Mean Method of the AHP as a robust technique for solving supplier selection-related problems.
The above paper is followed by another research paper by Ying Xie, Colin James Allen and Mahmood Ali, entitled “An integrated decision support system for ERP implementation in small and medium sized enterprises”. Over several years, enterprise resource planning (ERP) systems have supported large and multinational organisations in automating their core operations including optimising the flow of information and resources throughout the entire supply chain (Umble et al., 2003; King and Burgess, 2006; Raymond and Uwizeyemungu, 2007). Despite the many success stories (e.g. ranging from the airline industry through to the construction sector, implementing ERP is still considered as a challenging task specifically in SMEs (Sun et al., 2005). The authors argue that in general when ERP employment is considered, there are several parameters and dynamics affecting its successful implementation and as a result, it becomes demanding to develop a clear-cut mathematical model measuring the relationships among these parameters and dynamics. In line with these literature conceptions, this research develops an integrated decision support system for ERP implementation (i.e. DSS_ERP) to facilitate resource allocation and risk analysis in SMEs. This system is a combination of:
analytical regression models;
a simulation model; and
a nonlinear programming model.
Then we have Mahmud Akhter Shareef, Vinod Kumar, Uma Kumar and Yogesh Dwivedi presenting their research paper, entitled “Factors affecting citizen adoption of transactional electronic government”. This research is about investigating the adoption criteria of citizens’ e-government services at the transaction maturity stage. Gottschalk (2009) argues that from the developer's and users’ perspective, different e-government stages, i.e. service maturity and offerings have substantial differences in:
technological association; and
security and privacy concerns.
Thereafter, we have another research paper by Poonam Garg and Divya Agarwal, entitled “Critical success factors for ERP implementation in a Fortis hospital: an empirical investigation”. In this paper, Poonam and Divya conduct their empirical research in Fortis Healthcare – a well-established chain of super speciality hospitals in Delhi, Bangalore and across others parts of India. Healthcare is considered as one of the fastest growing sectors in India. According to Fitch – the rating agency (as reported in this paper), the healthcare sector in India is currently valued to be worth US$65 billion and is expected to reach US$100 billion by 2015. Similarly and according to Pricewaterhouse Coopers in its study, the healthcare sector is expected to touch US$250 billion by 2020. Over the years, the trend has shifted to a more intricate paradigm in response to maximising profits, reducing costs and achieving a seamless healthcare continuum. In this context, it has become imperative to improve the efficiency of back-end business across functions such as supply chain management, inventory management, human resource management, patient administration management, patient care management, finance and billing, etc. The authors argue that this can be accomplished by business process optimisation and technology enablement through successful hospital ERP implementation. In so doing, this research examines the success of ERP implementation based on five identified items:
top management commitment;
business process reengineering;
project management; and
ERP teamwork and composition factors at Fortis hospital, Bangalore, India.
Subsequently, we have Poonam Garg and Atul Garg presenting a research paper, entitled “Factors influencing ERP implementation in retail sector: an empirical study from India”. In this paper, Poonam and Divya focus on the retail sector where integration of the various business functions is considered as an essential prerequisite for any retail organisation. Poonam and Divya also report that retail chain in India heavily depend on ERP-based infrastructure to track the:
sales and distribution;
overall visibility of the consumer across every channel; and
take customer centricity to a new level.
The above ERP implementation research is following by another survey-based research by Rakhi Tripathi and M.P. Gupta, entitled “Evolution of government portals in India: mapping over stage models”. Over the last decade, a number of e-Government stage models have been proposed. For instance, according to Coursey and Norris (2008) a number of these models are partially descriptive, somewhat foretelling and relatively normative. Sandoval-Almazan and Gil-Garcia (2012) argue that in essence all these models attempt to describe what might be considered as the normal evolution of e-Government from its most basic online presence (i.e. static web pages, cataloguing) to fully developed e-Government (i.e. transformational government). In examining these models, it appears that, for the most part, the descriptions in these models provide a reasonably accurate portrait of e-Government in its early stages, from initial web presence to information provision to interactivity. Beyond this, however, the models become both predictive and normative and their empirical accuracy declines impulsively. In line with these conceptions, this paper attempts to examine conception, i.e. whether the e-Government stage models developed over the last one decade offer a factual indication of e-Government development in a developing country like India. A number of methods can be used to assess e-Government stage model for instance:
historical analyses; and
Then we have Dana Al-Najjar and Basil Al-Najjar with there research paper, entitled “Developing a multi stage predicting system for corporate credit rating in emerging markets: Jordanian case”. Dana and Basil report on the significance of credit risk forecasting by stating that it has gained substantial concerns due to financial market instability and the globalised capital markets. Recently, however, a number of research studies have devoted their focus to developing prediction models for credit ratings (Manganelli and Engle, 2001; Schuermann, 2004; Huang et al., 2004). Credit ratings are imperative for organisations because of the high risk associated with the incongruous credit decisions that may result in great financial damages. The literature indicates different techniques that can be used to examine credit ratings, for instance:
artificial intelligence approaches;
multi-criteria decision-making methods;
mathematical programming techniques;
linear regression analysis;
logistic regression modelling; and
linear discriminate analysis.
Finally, we have Hemlata Gangwar, Hema Date and A.D. Raoot presenting their review paper, entitled “Review on IT adoption: insights from recent technologies”. This review paper examines research studies from 2000 to 2012 by addressing technology adoption in the context of:
electronic data interchange; and
We would very much like to thank all our contributors for their excellent high-quality contributions to this regular issue and hope JEIM readers will find the submissions stimulating, original and valuable.
Zahir Irani and Muhammad Kamal
Coursey, D. and Norris, D. (2008), “Models of E-Government: are they correct? An empirical assessment”, Public Administration Review, Vol. 68 No. 3, pp. 523-536
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Gottschalk, P. (2009), “Maturity levels for interoperability in digital government”, Government Information Quarterly, Vol. 26 No. 1, pp. 42-50
Hassini, E. (2008), “Building competitive enterprises through supply chain management”, Journal of Enterprise Information Management, Vol. 21 No. 4, pp. 341-344
Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H. and Wu, S. (2004), “Credit rating analysis with support vector machines and neural networks: a market comparative study”, Decision Support Systems, Vol. 37 No. 4, pp. 543-558
Jansen, J., de Vries, S. and van Schaik, P. (2010), “The contextual benchmark method: benchmarking e-government services”, Government Information Quarterly, Vol. 27 No. 3, pp. 213-219
Johnson, M. (2012), “A study of e-market adoption barriers in the local government sector”, Journal of Enterprise Information Management, Vol. 25 No. 6, pp. 509-536
Kerr, N.L. and Tindale, R.S. (2004), “Group performance and decision-making”, Annual Review of Psychology, Vol. 55, pp. 623-655
King, S.F. and Burgess, T.F. (2006), “Beyond critical success factors: a dynamic model of enterprise system innovation”, International Journal of Information Management, Vol. 26 No. 1, pp. 59-69
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Raymond, L. and Uwizeyemungu, S. (2007), “A profile of ERP adoption in manufacturing SMEs”, Journal of Enterprise Information Management, Vol. 20 No. 4, pp. 487-502
Saaty, T.L. (1980), Multicriteria Decision-Making: The Analytic Hierarchy Process, McGraw-Hill, New York, NY
Sandoval-Almazan, R. and Gil-Garcia, J.R. (2012), “Are government internet portals evolving towards more interaction, participation, and collaboration? Revisiting the rhetoric of e-government among municipalities”, Government Information Quarterly, Vol. 29 No. 2, pp. 72-81
Schuermann, T. (2004), “A review of recent books of credit risk”, Federal Reserve Bank of New York, Vol. 10 No. 1, pp. 1-11
Shareef, M.A., Kumar, U., Kumar, V. and Dwivedi, Y.K. (2011), “E-government adoption model (GAM): differing service maturity levels”, Government Information Quarterly, Vol. 28 No. 1, pp. 17-35
Singh, A. and Wesson, J. (2009), “Evaluation criteria for assessing the usability of ERP systems”, Proceedings of the 2009 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists, pp. 87-95
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Sun, A.Y.T., Yazdani, A. and Overend, J.D. (2005), “Achievement assessment for enterprise resource planning (ERP) system implementations based on critical success factors (CSFs)”, International Journal of Production Economics, Vol. 98 No. 2, pp. 189-203
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