# Measuring Organizational-Fit Through Socio-Cultural Big Data

**Authors:** Narasimha Rao Vajjhala (Department of Computer Science and Software Engineering, American University of Nigeria, Yola, Adamawa State, Nigeria) — ORCID 0000-0002-8260-2392; Kenneth David Strang (School of Business and Economics, State University of New York, Queensbury, NY, USA; APPC Non-Profit Research, Australia) — ORCID 0000-0002-4333-4399
**Type:** Journal article
**Source:** New Mathematics and Natural Computation, 13(2), pp. 145–158
**Published:** 2017-07-03
**DOI:** https://doi.org/10.1142/S179300571740004X
**Canonical page:** https://www.narasimharao.net/research/organizational-fit-socio-cultural-big-data/
**Indexing:** Scopus · Q3 · Web of Science · ESCI
**Keywords:** Big data; qualitative; organizational behavior; culture; organizational-fit; decision-making

## Summary

**Question.** How could socio-cultural big data be collected and analyzed to measure organizational-fit factors relevant for human resourcing, partnering and other organizational decisions?

**Finding.** From a literature review the authors propose redefining a big data 'V' – viability – as a socio-cultural dimension, a lens for whether big data interpretations will generalize meaningfully to the intended population. They propose a two-phase approach: first sample big data to reduce volume and velocity challenges and apply data reduction (e.g. factor and cluster analysis); then formulate and test specific hypotheses with parametric or nonparametric statistics to make inferential generalizations for decision-making.

**Why it matters.** Collecting and analyzing big data requires large economic and time investments, so the authors argue it makes no sense to do so if the results will not be socio-culturally meaningful for the population of interest. Publicly available social networking data could let researchers test cultural dimensions such as uncertainty avoidance and individualism–collectivism, informing decisions on hiring, partnering and technology acceptance, though researchers must be cautious about big data quality.

## Key findings

1. The authors' literature review found no published studies examining the relationship of global culture behavior with organizational fit using big data sources.
2. The paper proposes 'viability' as a new big data 'V' representing a socio-cultural lens, alongside volume, velocity, variety, veracity and value, to judge whether big data interpretations generalize meaningfully to the intended population.
3. The study proposes a two-phase approach to socio-cultural big data: phase one samples big data to reduce volume and velocity and applies data reduction such as factor or cluster analysis; phase two formulates and tests hypotheses to make inferential generalizations.
4. For big data samples, the paper recommends normality tests such as Kolmogorov–Smirnov, Anderson–Darling, Ryan–Joiner or Shapiro–Wilk to decide whether parametric statistical tests may be used, and p-values or triangulation to judge veracity.
5. The paper suggests organizational culture fit could be measured with global culture models such as Gudykunst's cultural variability in communication framework, emoticon-based measures of individualism versus collectivism, or Vajjhala and Strang's socio-cultural collaboration strategies.
6. The authors recommend that researchers use publicly available Twitter and Facebook datasets to study cultural dimensions such as uncertainty avoidance and individualism–collectivism for organizational-fit decisions.
7. The paper cautions that big data collected from a country or geographical area may not represent the inhabitants of that location alone, so researchers must be careful when using and analyzing big data.

## Study at a glance

| Item | Detail |
|---|---|
| Research question | How can socio-cultural big data be used to measure organizational fit for decision-making? |
| Design | Conceptual paper based on a literature review |
| Data | Published literature on big data, organizational culture and national culture models (Hofstede, Trompenaars and Hampden-Turner, GLOBE, Competing Values Framework) |
| Methods | Development of a conceptual model and a two-phase research approach using sampling, data reduction and hypothesis testing |
| Main result | A new socio-cultural 'viability' dimension for big data and a two-phase approach to study socio-cultural big data |
| Implication | Big data should be collected only when its socio-cultural meaning is relevant and generalizable to the population of interest |

## Abstract

We propose that businesses, government, and not-for-profit entities could benefit from a better understanding of organizational behavior through the lens of a contemporary global culture model. Human resourcing and partnering decisions could be improved by using global culture to ensure a better organizational-fit as well as to reduce the risk of destructive relationship dependencies. For an extreme-limits example, a company could inadvertently hire a terrorist or a social loafer seeking to steal competitive intelligence. A big data approach supported by a socio-cultural framework could help in hypothesis testing which is essential for advancing the body of knowledge in organizational behavior. This paper will make a scholarly contribution by identifying literature relevant to collecting and analyzing organizational big data that could explain beneficial socio-cultural behavior. This paper will explore how sources of qualitative big data could be collected and then analyzed to measure organizational-fit factors relevant for decision-making.

## Key terms

- **Big data:** Data too big for conventional systems such as relational database management systems, characterized by volume, velocity, variety, veracity and value.
- **Organizational fit:** The degree to which an individual or partner matches an organization, which the authors propose could be measured through online personality and socio-cultural behavior.
- **Viability (socio-cultural dimension):** The authors' proposed big data dimension referring to the socio-cultural relevance of big data, based on where it was collected, and whether its meaning will generalize to the intended population.

## How to cite

Vajjhala, N. R., & Strang, K. D. (2017). Measuring Organizational-Fit Through Socio-Cultural Big Data. New Mathematics and Natural Computation, 13(2), 145–158. https://doi.org/10.1142/S179300571740004X

```bibtex
@article{vajjhala2017organizational,
  title = {Measuring Organizational-Fit Through Socio-Cultural Big Data},
  author = {Vajjhala, Narasimha Rao and Strang, Kenneth David},
  journal = {New Mathematics and Natural Computation},
  volume = {13},
  number = {2},
  pages = {145--158},
  year = {2017},
  publisher = {World Scientific Publishing Company},
  doi = {10.1142/S179300571740004X},
  url = {https://doi.org/10.1142/S179300571740004X}
}
```