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The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

Quality management practices (QMP) have stood as one of the critical strategic differentiators for enhancing firm performance. The production and manufacturing industry is the main driving force of economic growth and social development for any developed or developing country. This study aims to focus on two primary dimensions of QMP: soft quality management practices (SQMP) and hard quality management practices (HQMP) from the socio-technical system perspectives. Based on institutional theory perspectives, the study explores the impact of SQMP and HQMP on quality performance (QP), innovation performance (IVP) and financial performance (FP) in Indian oil processing organizations.

A proposed research model is validated using 289 cross-sectional survey data collected from the senior officials of oil processing firms in India. Covariance-based structural equation modeling is used to verify the proposed theoretical model.

SQMP, directly and indirectly, influenced QP and IVP while only indirectly to FP mediated through QP. HQMP directly impacted only QP while indirectly to IVP and FP mediated through QP.

Impact of organizational legitimacy in proper utilization or application of QMP in achieving the firm sustainable growth. The future study may address the following Research Question (RQ) also: How do QMP enhance the legitimacy of organizations operating in the oil processing industries? Are there specific mechanisms or pathways through which improved performance contributes to enhanced organizational legitimacy? How does legitimacy impact the success and sustainability of organizations, particularly, within the context of the oil processing industries? Are there regulatory requirements or industry certifications that organizations must adhere to in order to maintain legitimacy?

Similarly, manufacturing firms establish QMP of interaction and maintaining relationships with all the stakeholders, total employee empowerment and involvement, workforce commitment and workforce management, helping to control their reputations and maintain legitimacy (Li et al., 2023). Similarly, in the health industry, the health management information system (HMIS), which uses the DHIS2 platform, establishes that isomorphism legitimizes data QMP among health practitioners and, subsequently, data quality. Further, it was concluded that mimetic isomorphism led to moral and pragmatic legitimacy. In contrast, normative isomorphism led to cognitive legitimacy within the HMIS structure and helped to attain the correctness and timeliness of the data and reports, respectively (Msendema et al., 2023). Quality, flexibility and efficiency of Big Data Analytics through better storage, speed and significance can optimize the operational performance of a manufacturing firm (Verma et al., 2023).

The study provides the academician with the different dimensions of QMP. The study demonstrates how a firm develops multiple performance capabilities through proper QMP. Also, it shows how vital behavioral and managerial perspectives are to QMP and statistically solid tools and techniques. The study draws their importance to risk factors involved in the firms. Since the SQMP play a vital role, thus, emphasis on the behavioral dimension of quality requires more investigation and is in line with hard technological advancements in the quality field.

The study of the impact of HQMP and SQMP on performance is still not established. There are inconsistencies in the findings. The study of the impact of HQMP and SQMP in oil processing industries has not dealt with before. The effects of HQMP and SQMP on the firm’s FP have least been dealt. In context to the intended influence of QM implementation, QP has not been examined as a potential mediator between FP. Research carried out in the past is limited to American and European countries. However, a limited study was done in Asia, and no study has been conducted in the Indian context.