State of the art reviewMaturity models in LIS study and practice
Introduction
The literal meaning of maturity is ‘ripeness.’ The term “maturity” traces the evolution of a phenomenon from the initial to the advanced stage (Fraser, Moultrie, & Gregory, 2002). Proença, Vieira, and Borbinha (2017) explain maturity as a state of completeness; however, it is not an end but perceived as evolutionary progress in the pursuit of a specific goal (Proença & Borbinha, 2016). Its nature is dynamic (Proença and Borbinha, 2018). Maturity evaluation is one of the conventional approaches to decide the sophistication level of a system, service, or process (Kouper, Fear, Ishida, Kollen, & Williams, 2017). A maturity model is a tool that helps to assess the current status and future prospects of any process, person, or group (Fowler, 2014). Maturity models help figure out the capabilities required to improve performance. Maturity models have been proposed for various activities such as quality management, software development, supplier relationships, process management, R&D effectiveness assessment, product design & development, and collaboration (Becker, Knackstedt, & Pöppelbuß, 2009). Maturity models are also used for auditing, benchmarking, progress assessment, and performing a SWOT analysis of an organization or a process.
Generally, maturity models have levels and attributes as defining elements; however, it (varying maturity model) may include model areas, evaluation & scoring techniques, and improvement roadmaps depending on the domain and purpose. In a maturity model, levels represent the evolution stages of a phenomenon. A model domain is a category of similar attributes, whereas an attribute is the core content of a model that connects the domain and levels. The appraisal & scoring methods ease the model assessment using specific algorithms (Caralli, Knight, & Montgomery, 2012). The majority of maturity models use Deming's PDCA (plan, do, check, act) cycle to improve the road map (Deming and Renmei, 1952).
Resources, patrons, and staff are three significant components of libraries and information centers (LICs). One of the challenges of LICs is the effective and efficient management of its components. LICs provide a wide range of services such as reference services, information literacy tutorials, research data services, awareness lectures, etc. Maturity models are used as an assessment tool or a guide to initiate a project or service in a domain (Khoshgoftar & Osman, 2009). Libraries and information centers can use the capabilities of maturity models for assessing and improving their management process and services. The maturity models developed or employed for the domain Library and Information Science (LIS) are the focus of the present study.
Despite extensive research on maturity models, there are significantly fewer efforts for their review. This study identified that there is no systematic review of LIS maturity models. This study attempts to determine the core literature on LIS maturity models systematically and identify various parameters for evaluating a maturity model. Doing so would determine the current practices and future prospects of the maturity models employed or developed for LIS. This review is relevant for researchers, maturity model developers, funding agencies, and policymakers in general and to information professionals in particular. The comparative analysis amongst the models is useful for adopting a relevant maturity model for information centers. This study also points out various features of LIS maturity models that are critical for the maturity model developers to improve an existing or in developing a new model.
Section snippets
Maturity models
A maturity model structures the pathway of the progress of an organization and process. Maturity models are alternatively known as stages-of-growth models, stage models, or stage theories (Proença et al., 2017). Maturity models set up a benchmark used by the organization to determine their current capabilities and expected future improvements (Caralli et al., 2012). Pöppelbuß and Röglinger (2011) define the maturity model as “a series of sequential levels, which together form an anticipated or
Methodology
Systematic literature review (SLR) is an established method of scientific research. SLR enables a researcher to identify, evaluate, and interpret the existing studies to investigate a topic or research question (Kitchenham et al., 2009). It allows researchers to review multiple studies in a single attempt (Samsuddin, Shaffril, & Fauzi, 2019). SLR method is relevant for gap identification and to review the current trends and future direction of a study.
The present study's SLR method adopted the
Findings
The second review question for identifying the specification of LIS maturity models was answered using a parametric approach.
Discussion
Descriptive parameters are useful in the discovery, display, and interoperability of a maturity model. Therefore, the study of the descriptive specification is significantly critical. Based on the parametric approach, the meta-analysis of accepted documents reveals that there are only a few LIS aspects for which the maturity models have been developed or employed. The study found many potential LIS areas for maturity models such as information services, sources, personnel management, systems,
Conclusion
This study identifies the core literature on maturity models developed for or adopted by the LIS domain (RQ1). The study reviews the selected maturity models using a parametric approach (RQ2). An SLR method was adopted to conduct the study. The SLR method is free from the selection biases; therefore, this method was preferred over a narrative review for the study. The study's findings are critically relevant to both the LIS practitioners and maturity model developers or managers. LIS
Declaration of Competing Interest
None.
Acknowledgment
Authors would like to thank the editor and anonymous reviewers for their valuable suggestions and comments.
Amit Tiwari is a research fellow at Documentation Research and Training Centre (DRTC), Indian Statistical Institute, India. He is a doctoral student in Department of Library and Information Science at Calcutta University, Kolkata, India. Mr. Tiwari earned a master's degree in Library and Information Science at the Indian Statistical Institute. His interest areas are data management and curation, data representation, digital libraries, semantic web technologies, and knowledge organization.
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Amit Tiwari is a research fellow at Documentation Research and Training Centre (DRTC), Indian Statistical Institute, India. He is a doctoral student in Department of Library and Information Science at Calcutta University, Kolkata, India. Mr. Tiwari earned a master's degree in Library and Information Science at the Indian Statistical Institute. His interest areas are data management and curation, data representation, digital libraries, semantic web technologies, and knowledge organization.
Dr. Devika P. Madalli is a professor and head of the DRTC, Indian Statistical Institute, India. She holds PhD in Library and Information Science from Mysore University, India. She is a member of the technical advisory board of Research Data Alliance. Dr. Madalli has served as a consultant to UNESCO and UNFAO. She has worked as collaborator, consultant and advisor with several international institutes and agencies such as Universal Decimal Classification, IEA, ACU, CoL, OECD, CODATA among others. The author has published in Journal of Informetrics, Journal of Documentation, Journal of Knowledge Management, and Knowledge Organization.