Big data management capabilities and librarians' innovative performance: The role of value perception using the theory of knowledge-based dynamic capability
Introduction
Big data management is a field that requires adequate planning (Harwell, Vivian, McLaughlin, & Hafner, 2019) and investment (Heripracoyo et al., 2019), both in technology (Ma et al., 2019), training (Corti et al., 2019) and economically (Ruan et al., 2019). Although policies may be relevant to the management of big data in institutions such as universities, there is no escaping the fact that libraries in universities have enormous data to manage, hence the relevance of this subject, regardless of the availability of policies (Pucci et al., 2019). Literarily, the more knowledgeable a librarian is about the management and the value of big data, the more likely he gets to be more innovative with the management and use of big data (Zheng et al., 2011). Although developing countries have many development needs which may cloud the need to invest in big data technologies in the short run, it is an area which is gaining grounds gradually although currently of less priority (Rojas-Torres & Kshetri, 2019). This could explain the low attention paid to big data management challenges on general basis as the institutional level. For instance, in Ghana, it is difficult to specifically define data management policies that regulate the management of big data, both at the national level and institutionally, despite strides being made in recent times (Emetere, 2019). Many universities in Ghana, due to this general challenge, are likely to not have comprehensive policies that regulate this subject area.
The importance of big data management cannot be overemphasized in this study and in the 21st century (Anthony Jnr et al., 2019; Corti et al., 2019). Big data are able to reveal trends that are essential to discoveries made about the use of information (Faoury et al., 2019), likeability and acceptability of products and services (Dubey et al., 2019), reduced interest in certain types of products and services, shifts towards certain types of products and services, and in general, the dynamism in consumer behaviour over long periods of time (Inanc-Demir & Kozak, 2019). However, the extent of usage and the importance of big data to an institution is dependent on the value placed on big data modulated by their perception as well as results gained from actual usage of big data (Egan & Haynes, 2019; Shamim, Zeng, Shariq, et al., 2019). Also, at an individual level, the innovative performance of individuals in the field of information management can be modulated by value and perception towards big data (Heripracoyo et al., 2019; Zheng et al., 2011). This can be based on the theory of knowledge- based dynamic capabilities put forward by (Zheng et al., 2011).
Dynamic capability as a theory, is one of the most important in the area of strategic management and has been based mostly on the resource-based view that describes firms' ability to renew their resource-based competitive advantage dynamically (Shamim, Zeng, Choksy, et al., 2019). In this context, however, knowledge-based dynamic capability describes the ambidexterity and innovative characteristics that could immerge as a result of knowledge gained about a subject or about subjects relevant to a particular field. Specifically, in the arena of big data management as a field with which librarians can be identified, adequate knowledge can act as a catalyst to manifest dynamic characteristics that show innovation among librarians (Shamim, Zeng, Choksy, et al., 2019). In this study, the direct effect of big data management capabilities as a function of knowledge-based dynamic capabilities on librarian innovative performance was investigated. The mediating effect of big data value perception on this relationship was also studied.
Section snippets
Concept of knowledge-based dynamic capability and hypothesis development
As described by (Zheng et al., 2011), dynamic capabilities are the ability of an individual to acquire, generate and combine knowledge-based resources in order to make sense of, to explore and to adapt to environmental changes. To add, this ability to address issues based on a changing environment is supposed to give some level of competitive advantage to the individual or institution generally. A firm's ability to adapt and renewing its strategies gives them an added advantage among others and
Sampling and data collection
The quantitative method of enquiry was adopted for this study using a structured questionnaire and guided interviews to collect cross-sectional data from librarians from selected Ghanaian universities. Universities are such places where big data are generated on yearly basis and the library is a hot spot for large digital data on both faculty and student activities in the library. In order to facilitate the data collection, this study only focused on librarians who are in charge of data
Data fitness
The statistical validity all study constructs were assessed by their Cronbach's alpha values and this was done to ensure that constructs were reliable and had internal consistency. From Table 1, the Cronbach's alpha values for all the study variables measured were higher than 0.8, showing that the variables were having high covariances, reflecting good reliability and statistical fitness for PLS-SEM method. Convergent validity was tested and the factor loadings for each variable in constructs
Conclusion and recommendations for future research
The theory of knowledge-based dynamic capabilities has been extended through its application to individual employees instead of the organization as usually done. In this study, it was found that big data management capabilities alone did not significantly influence the innovative performance of the librarians. Rather, these capabilities had significant influence on the value librarians place on big data management, resulting in a mediation function of value perception observed. Thus, when
CRediT authorship contribution statement
Isidore Komla Zotoo: Conceptualization, Methodology, formal analysis, writing, Original draft preparation.
Zhangping Lu: Methodology, Investigation. Supervision, Validation and Resources.
Guifeng Liu: Reviewing, Writing, Software and Editing.
Declaration of competing interest
There is no conflict of interest in this project.
Acknowledgements
This work was supported by the Chinese National Social Science Foundation (Nos. 16BTQ004, 17BTQ025) and the Philosophy and Social Science Fund of Education Department of Jiangsu Province (No. 2019SJA1871)
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