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Developing countries are predicted to have more than three-fourths of the world population because, by 2030, 85% of the population is expected to live in these countries (Goel et al., 2021). Meanwhile, big data analytics has increasingly become affordable for the population to solve critical problems and generate value propositions. Most importantly, big data analytics is urgently needed for developing countries to increase their gross domestic product (Goel et al., 2021) and to cope with the technology transfer from China and the USA. Although there is no unanimity on the big data Vs, the literature typically addresses the major Vs: volume, veracity, variety, velocity, variability, and value proposition (Dubey et al., 2022; Ghasemaghaei & Calic, 2020; Mazanec, 2020; Youssef et al., 2022; D. Zhang et al., 2022). Across all disciplines, big data analytics continues to grow, and one of its critical roles is to generate value propositions by providing insights from unstructured big data sets (Dubey et al., 2022).
According to Zhang et al. (2022), the USA, China, the United Kingdom, and Canada are the most productive contributing countries in big data analytics research. Most importantly, existing theorists have built and validated measurement instruments to assess the following big data Vs: volume, variety, velocity (Johnson et al., 2017), veracity (Ghasemaghaei & Calic, 2019), and value proposition (Ghasemaghaei, 2021). However, authors have paid little attention to this type of research in the developing world. For instance, rare big data research was conducted about the Ebola virus outbreak in West Africa (Mir et al., 2022). The measurements of big data variability and the value proposition of big data analytics of the progression of this disease were ignored. Also, the complexity of analyzing big data corroborates the challenge of measuring its Vs or characteristics. Additionally, big data's unpredictable and unexpected growth poses measurement challenges to big data analytics for individuals, organizations, communities, and governments (Onyeji-Nwogu et al., 2020). Moreover, traditional tools and instruments fail to address these challenges and measure the benefits regarding the big data value proposition in developing countries that share sub-Saharan African culture.
Culture can strongly determine how individuals embrace digital transformation in developing countries (Lawson-Body et al., 2016). Hofstede's theory of cultural dimensions and their Value Survey Module 1994 (VSM-94) measurement instrument did not include technological factors and developing countries’ cultural underpinnings (Lawson-Body et al., 2016). Consequently, limited existing studies of big data analytics measurement validation did not include developing countries' cultural factors. These few studies were conducted as if there is no cultural distance between developed and developing countries regarding big data analytics consumption. Because of Hofstede's cultural distance theory (Pelau & Pop, 2018), big data analytics for developed countries cannot be considered for measuring the big data value proposition appropriate to individuals residing in developing countries. Therefore, this study fixes the inadequacy and lack of instruments to measure the benefits of big data analytics for these growing and culturally different populations living in developing countries. This research aims to fill the current big data analytics literature gap.
As a result, this study enriches the literature by answering the following research questions:
- 1.
How can Hofstede's theory of cultural dimensions and VSM-94 be used as a theoretical foundation for a big data analytics measurement framework in developing countries?
- 2.
How can big data Vs be augmented with cultural dimensions appropriate to developing countries?
- 3.
What are the new instruments for measuring big data Vs and cultural big data analytics, and what are their relationships with developing countries’ big data value proposition?