Introduction and Background
Companies worldwide have adopted lean practices, and there has been a significant focus on the connection between lean manufacturing practice and organizational success. The goal of Lean manufacturing is to maximize consumer value while reducing waste. The ultimate aim of introducing lean production in a business is to boost efficiency, improve quality, minimize lead times, and lower costs. Previous research has concentrated on the technical side of lean practice implementation and its effect on results rather than the “people” issues (Onofrei et al., 2019). Researchers recently turned their attention to “why” lean succeeds (or does not), with a specific focus on human resource management (HRM) techniques.
Companies must concentrate on developing committed and specialized expertise and forming a larger institutional aggregation of intellectual capital (IC) to achieve long-term lean implementation. Awareness and IC management have become critical factors in a company’s growth and survival, making it adaptable and receptive. Personnel, organizational routines, production procedures, and partnerships around the supply chain are examples of knowledge-based tools. According to the article, to maximize investments in lean practices, organizations must understand how to utilize various operational intellectual capital (OIC) dimensions (Onofrei et al., 2019). Since they are uncommon, valuable, and difficult to replace or copy, such knowledge-based tools provide a competitive advantage.
Research Question
The article investigates the interactions between OIC dimensions and investment in lean planning (ILP). The research question can be stated as, “Is it possible to improve the efficacy of ILP by using OIC?” This question addresses the research gap between ILP and OIC (Onofrei et al., 2019). The authors hypothesize that OIC is a critical knowledge-based asset that is essential, difficult to duplicate or replace, and produces powerful operational and competitive advantage when properly leveraged.
Data Used
The data used in this study is derived from the Global Manufacturing Research Group (GMRG) fifth round’s survey project. The data contained demographic information, sustainability, competitive environment, innovation, supply chain management, and organizational culture. The data relates to 528 out of the 987 respondents who answered all the questions in the modules (Onofrei et al., 2019). As a result, the GMRG data are suitable for interpreting the synergism between ILP and OIC parameters due to their broad coverage across ten countries of all major continents.
Qualitative Vs. Quantitative Study
Qualitative and quantitative studies differ by the nature and type of data analyzed. Qualitative data is expressed in words and is used to generate an in-depth understanding of concepts. Most of this data is obtained from secondary sources such as peer-reviewed journals. On the contrary, quantitative data is expressed in numerals and visual representations such as graphs. The quantitative study is focused on confirming theories using primary sources of data such as experiments and observation. The article uses a quantitative study since its data source is a survey whereby individual data was collected from 987 persons and analyzed through t-tests. Although the authors referred to several researcher’s theories, the data were independently analyzed, and control variables were used appropriately (Onofrei et al., 2019). Using industry type and plant size as control variables and the application of confirmatory factor analysis, the authors proved the connection between ILP and OIC constructs.
Sampling Method
In the article, the researchers used a stratified sampling method to gather and evaluate their data. Stratified sampling When researchers are trying to draw conclusions from various sub-groups or strata, they also use stratified sampling. The strata or sub-groups should be distinct, and there should be no overlap in the results. The researcher can apply basic probability sampling when using stratified sampling. Age, ethnicity, gender, work profile, nationality, educational level, and other factors are used to divide the population into subgroups.
In this article, the researchers identified more than ten countries from each major continent and designed questionnaires for each country. In this case, nationality was the factor used to group the population sample. From each country’s data, the respondents who filled all the modules included in the questionnaire were picked for inclusion into the central database (Onofrei et al., 2019). This implies that only data contained fully filled questionnaires from each country were used in the final data analysis.
The main reason behind researchers’’ use of the stratified sampling method is the need for extensive coverage. Selecting countries from each major content provided a wide range of data that implies the samples were likely to be a more accurate representation of the entire population. Also, the researchers needed to establish the different factors influencing OIC parameters in different countries and their influence on ILP.
Conclusions from the Study
The moderating function of OIC was considered in the study of the effects of ILP on operational efficiency. The findings support the effect of social and structural capital (STC) on ILP and operational performance improvement. The findings help to clarify the deep connection between ILP and operational success. In terms of application, the study provides managers with empirical data on the impact of knowledge management on organizational efficiency. The study focuses on how the OIC’s unique features can be used to supplement operational output provided by ILP.
Validity and Reliability
The research is founded on the thesis that better operational efficiency is achieved by investing in operational intellectual capital and lean practices. The study presents empirical evidence obtained from quantitative analysis of research surveys to show how intellectual capital can be used to influence lean practices. The researchers applied stratified sampling to obtain and analyze data from over ten countries in major continents. The results show that human capital (HUC), structural capital (STC), and social capital (SOC), will enhance lean investments’ performance.
Confirmatory factor analysis was applied in validating the measures relating to all variables in the study. The elements’ coefficients and standard errors were compared to see if they were convergent. The results show that each coefficient was more than double its standard error. Furthermore, the chi-square to degrees of freedom ratio of 2.209 (2/df) is appropriate (Onofrei et al., 2019). In each category, composite reliability figures revealed high build reliability; all values are well above 0.7. At both the build and item stages, the findings showed that the measures had sufficient discriminant validity. These findings are considered to be valid and reliable particularly given the data set’s multi-nationality, diversity and spread across various industries.
The study has several limitations that may limit the findings’ validity and reliability. First, the measurement items for OIC must be refined in order to capture the measurements of OIC holistically. Second, the study looked at the effect of ILP on operational efficiency without taking into account other performance indicators like economic and market-related performance (Onofrei et al., 2019). Third, considering how far an organization has gone on its lean path, the review offers few theoretical insights into the use of OIC capital differently. Finally, the nature of the research sample framing is unlikely to be random. Despite the advantages of a large-scale multi-state data set, the data collection is restricted, which must be taken into account when analyzing the findings.
Reference
Onofrei, G., Prester, J., Fynes, B., Humphreys, P., & Wiengarten, F. (2019). The relationship between investments in lean practices and operational performance: Exploring the moderating effects of operational intellectual capital. International Journal of Operations & Production Management, 39(3), 406-428.Web.