Effect of Marketing Strategies on the Performance of Agribusinesses in North-Central, Nigeria

Effect of Marketing Strategies on the Performance of Agribusinesses in North-Central, Nigeria

 Aperaga Enoch Inyommom, Zacharias Ternenge Nyiatagher, Ali Ocholi, Emmanuel Iorfa Bai

Department of Agribusiness, Faculty of Management Sciences, Joseph Sarwuan Tarka University, Makurdi Benue State, Nigeria

DOI: https://dx.doi.org/10.47772/IJRISS.2024.801076

Received: 23 December 2023; Accepted: 04 January 2024; Published: 05 February 2024

ABSTRACT

This study investigates the influence of distinct marketing strategies—product, promotion, price, and place strategies—on the performance of agribusinesses in the North-Central region of Nigeria. Leveraging a sample size of 338 from a population of 2,216 registered agribusinesses across five selected states, a cross-sectional survey design was employed to analyze the relationships between the chosen independent variables and critical performance indicators namely; profit, sales volume, market share, and customer loyalty. The results from four regression models underscore the degree of impacts of marketing strategies on performance metrics. Notably, promotion and place strategies emerge as significant drivers of increased sales volume and enhanced customer loyalty, respectively. Conversely, product strategy exhibits a minimal effect on profit, while price strategy strongly influences market share. These findings offer actionable insights for agribusiness practitioners and policymakers to optimize their marketing approaches for improved performance and sustainable performance in the agribusiness sector.

Keywords: Marketing Strategies, Performance, Agripreneur, Market Share, Profit, Customer Loyalty,

INTRODUCTION

Marketing as a management function has been widely acknowledged across a range of industries as being crucial for boosting market share, customer loyalty, sales volume, long-term relationships, customer satisfaction, profitability, competitive advantage, and firm performance (Adamu, 2020). Chukwuemeka (2015) asserts that the attention and strategy employed by an organisation in its marketing efforts are crucial factors that contribute to its success or failure. In the context of Nigeria, a prominent market within Africa, the significance of efficient marketing cannot be overstated in fostering corporate expansion (Chukwemeka, 2015). Higher customer demands, a risky and unstable economic environment, internationalization of trade, and more competition, among other factors, have all contributed to increased competition in the Nigerian market. Chukwuemeka (2015) further stated that as certain businesses have shrunk, it has been harder for them to survive and be viable. As a result, there have been numerous reports of high business mortality in literature from throughout the world, with a focus on developing nations like Nigeria (Chukwuemeka, 2015). The creation of superior products or services, according to Adamu (2020), would not always ensure corporate success or boost performance if they are not supported by efficient marketing.

The concept of marketing strategy as a collaborative effort emphasizes the need for effective communication of an organization’s long-term position. Adamu (2020) asserts that any Agribusiness aiming to thrive in a competitive market must employ strategic marketing tactics. The relationship between supply and demand, regulated by the market, is fundamental in marketing (Kotlar & Armstrong, 2013). The marketing mix, including planning, production, pricing, promotion, and distribution, plays a crucial role in creating and exchanging value (Kotlar & Armstrong, 2013; Cross, 2018). Businesses use marketing tactics to acquire and retain customers, relying on strategic decisions for success (Kisaka, 2012). Marketing strategies, defined as techniques to differentiate and meet client demands, involve deliberate actions for a competitive advantage (Adamu, 2020). Agribusinesses, by maximizing product output and efficiently transporting goods to the market, contribute significantly to the contemporary food and agriculture landscape, emphasizing the need for strategic marketing efforts in this sector (Mehta, 2012).

The incorporation of marketing strategies in agribusinesses is crucial for maintaining competitiveness, and the type of strategy chosen may be influenced by market competitiveness (Eniola and Olorunleke, 2020). The objectives of agribusinesses, such as increasing sales volume and profitability, guide their marketing strategies (Abdulrahman et al., 2019). Agribusinesses, processing plant and animal materials, add value to agricultural commodities and invest in processing machinery, contributing to increased income and per capita food intake (Mehta, 2012). The rising demand for higher-value commodities, changing dietary patterns, and the use of agricultural products for bioenergy generation are influencing the agribusiness sector (FAO, 2007). Recognizing the socioeconomic impact of agribusiness, the World Bank (2007) emphasizes its role in economic development, food security, and poverty reduction. Agribusiness, considered the “engine of growth and catalyst” for socioeconomic transformation, creates jobs, enhances value, and promotes socioeconomic growth, particularly in rural areas (Onwumere, 2008). Moreover, the Central Bank of Nigeria (2011) underscores agribusiness’s potential to reduce import costs and generate employment in Nigeria while contributing to international trade through agricultural exports.

Agribusinesses, encompassing fish import and export, ocean trawling, fishing, poultry farms, hatcheries, and veterinary clinics, contribute significantly to the transformation of agricultural products into diverse forms, enhancing their value for various purposes (Mehta, 2012). These businesses, involved in farming, processing, and livestock production, play a vital role in increasing per capita food intake, creating employment, and promoting economic independence in developing nations like Nigeria (FAO, 2007). However, despite their potential, agribusinesses in Nigeria, particularly in the north-central region, face challenges related to the inadequate implementation of effective marketing strategies, hindering their market position, sales volume, and overall performance (Geoffrey, 2021; Ocholi et al., 2018). The lackluster performance of agribusinesses in Nigeria, especially in the north-central region, has been attributed to the failure to adopt effective marketing strategies (Geoffrey, 2021; Ibojo, 2015). The economic and political challenges in Nigeria further contribute to the suboptimal implementation of marketing strategies, resulting in a decline in sales volume, loss of profit, and the struggle for survival among agricultural enterprises in the north-central region (Ocholi et al., 2018). Despite the significance of agribusinesses in Nigeria’s economic history, the sector has faced a decline since the discovery of petroleum oil, leading to increased dependence on imported items and a substantial import bill (Ocholi et al., 2018). Several studies have explored the impact of marketing strategies on business performance in Nigeria, particularly in Benue State and other regions (Adamu, 2020; Cross, 2018; Gbolagade & Adesola, 2013; Eniola & Olorunleke, 2020). However, there is limited research on the specific challenges faced by agribusinesses in north-central Nigeria, highlighting the significance of this study in addressing the observed gap and contributing to the existing body of literature (Ocholi et al., 2018). To address these challenges and reverse the trend, there is a need to investigate the impact of marketing strategies on the performance of agribusinesses in north-central Nigeria, considering the existing knowledge gap in this specific context.

Objective of the study

The broad objective of this study is to examine the effect of marketing strategies on the performance of agribusinesses in North-Central, Nigeria. The specific objectives are to:

1. to examine the effect of product strategy on the level of profit of agribusinesses in North-Central Nigeria.
2. determine the effect of promotion strategy on sales volume of agribusinesses in North-Central Nigeria.
3. ascertain the effect of price strategy on market share of agribusinesses in North-Central Nigeria.
4. investigate the effect of place strategy on level of customer loyalty of agribusinesses in North-Central Nigeria.

Research Hypotheses

H0₁:    There is no significant effect of product strategy on the level of profit of agribusinesses in North-Central Nigeria. .

H0₂:    There is no significant effect of promotion strategy on sales volume of agribusinesses in North-Central Nigeria.

H0₃:    There is no significant effect of price strategy on market share of agribusinesses in North-Central Nigeria.

H0₄:    There is no significant effect of place strategy on customer loyalty.

LITERATURE REVIEW

Conceptual Framework

Marketing Strategies

The conceptual framework delineates the critical elements of marketing strategies, emphasizing their role in achieving a competitive advantage. Marketing strategy involves systematic decision-making to deploy resources effectively, with the primary goal of enhancing and maintaining a company’s competitive advantage (Gronroos, 1999; Owomoyela et al., 2013). Lin’s (1993) categorization identifies four types of marketing strategies, including dual-target, reasonable, emotional, and maintenance strategies, each catering to distinct consumer considerations. Furthermore, product strategy, a dimension of marketing strategies, focuses on aspects like product design and technology integration, recognizing the centrality of products in meeting customer needs (Kotler & Armstrong, 2013).

Dimensions of Marketing Strategies

The dimensions of marketing strategies encompass product, promotion, price, and place strategies, each contributing to the overall effectiveness of the marketing mix. Promotion strategies involve the dissemination of information to influence customer perceptions and behaviors, emphasizing the importance of aligning organizational functions to consumer needs (Keegan et al., 2002). Price strategy, influenced by factors like target market and product characteristics, plays a crucial role in determining perceived worth and profitability (Rapert et al., 2008; Lovelock, 2011). Place strategy considers geographical factors, distribution methods, and environmental elements, recognizing the significance of the physical environment in enhancing the consumer service experience (Lui et al., 2011). The framework underscores the interconnectedness of these dimensions within the marketing mix, with pricing identified as a key driver of organizational profitability (Lovelock, 2011).

Performance

Performance can be defined using two fundamental approaches: outcome-based methods and behavioural methods. The manifestation of appropriate and inappropriate conduct by employees within the workplace can be elucidated through the lens of behavioural methods to job performance, which seek to explicate the relationship between such behaviours and overall job performance. The outcome method is a perspective that evaluates work performance based on the achieved results, such as the volume of sales or acquisition of new clients. According to Cardy (2004), both the behavioural and outcome approaches to delineating and evaluating work performance possess their own merits and demerits. These two tactics have the potential to effectively convey to the staff the necessary requirements and tasks that need to be accomplished in the workplace. The expression of performance is contingent upon the specific technology and type of job analysis employed. The advent of technology has the potential to bring about substantial changes to several domains of responsibility, potentially leading to the complete elimination of particular tasks and responsibilities. The extent and diversity of technological utilization within an organization can significantly influence the characteristics of job roles and the methods employed to convey performance outcomes. The manner in which performance is portrayed is contingent upon the specific type of job analysis conducted. The manner and procedure of analysis can potentially influence the outcomes that are obtained.

Dimensions of Performance

The first dimension of performance is profit, which is the monetary benefit derived from the disparity between revenue and costs in the acquisition, operation, or production of a business entity (Cross, 2018). Profit is crucial for a company’s long-term viability, requiring assessment of prospective and past profitability, considering efficiency, solvency, and market prospects (Cross, 2018). Sales volume, the quantifiable measure of units sold, is often overlooked but considered significant for evaluating a company’s overall well-being and growth trajectory (Cross, 2018). Market share, representing the proportion of total sales volume in a market attained by a specific brand or enterprise, serves as a pivotal indicator of market competitiveness and aids in understanding consumer preferences (Cross, 2018). Decreases in market share may indicate underlying challenges, making it an early indicator of future opportunities (Farris et al., 2020).

The second part introduces customer loyalty, emphasizing the importance of personal communication between customers and vendors in fostering loyalty. Personal relationships and trust developed through cordial customer service contribute to customer loyalty (Praveen and Tanvi, 2015). Customer loyalty is characterized by both attitudinal and behavioral dimensions, with marketers categorizing clients based on their level of behavioral loyalty (Francis and Mathenge, 2015). The loyalty ladder model, consisting of suspicion, prospect, customer, client, and advocate, highlights the desirability of clients who exhibit higher levels of loyalty (Francis and Mathenge, 2015). While relationship-building methods are more challenging in transactional marketplaces, customer relationship management and direct marketing have become integral in establishing and maintaining customer loyalty (Praveen and Tanvi, 2015).

Agripreneur

Agripreneurs, defined as individuals focusing on launching, planning, and running businesses within the agricultural industry, play a vital role in adding value to agricultural resources, particularly in rural areas. This form of entrepreneurship is comparable to others but maintains a unique context within the agricultural sector. Agripreneurs seek new business opportunities, manage resources, take calculated risks, and ultimately succeed financially within the agricultural domain, requiring expertise in various areas such as strategic planning, market research, production, sales, and finance. Agribusiness services encompass crop advice, agricultural product sales, market introductions, and assistance in obtaining loans, contributing to the evolution of sustainable agriculture. With a presence in various sectors, including dairy, sericulture, horticulture, and fisheries, agripreneurs connect farmers to both urban and rural markets, fostering youth entrepreneurship and regional economic strength. The positive impacts of agribusiness entrepreneurship include facilitating regional economies, reducing rural poverty, increasing agricultural yields, lowering food costs, ensuring stability, and improving nutrition in both rural and urban areas. Overall, agribusiness entrepreneurship is instrumental in expanding business opportunities, supporting existing businesses, and diversifying income sources (Mukhopadhyay & Mukhopadhyay, 2020).

Theoretical Framework

Push – Pull Theory

Organizations often employ the push-pull theory in their marketing strategies, a concept originating from Fredrick Winslow Taylor’s principles of scientific management in 1911 (Zmud, 1984). The push-pull approach determines whether the customer or the company initiates the transaction, adapting marketing strategies throughout a product’s life cycle stages. Zmud (1984) suggests that push marketing is effective in the introductory and early growth stages, focusing on brand introduction, while pull marketing becomes crucial for businesses aiming to expand or maintain market share. A successful marketing strategy, emphasizing mutual value exchange with the target audience, involves a combination of push and pull tactics (Cooper & Kleinschmidt, 2015). Push marketing relies on actively presenting products to the audience through traditional advertising channels like radio, TV, print, and mail (Cooper & Kleinschmidt, 2015). It is a more demanding and costly strategy, suitable for short-term sales goals, such as holiday promotions. Pull marketing, on the other hand, centers on attracting customers to the business location, utilizing inbound strategies like content creation, SEO, and social media. Cooper and Kleinschmidt (2015) emphasize the long-term effectiveness of pull marketing, highlighting its less intrusive nature and alignment with consumer behavior. The push-pull theory recognizes the synergy of both approaches, providing a powerful strategy for lead generation and conversion in marketing, applicable to optimizing processes in agribusiness for increased performance and sustained competitiveness.

Empirical Review

Effects of Product Strategy on Profit

Destya et al (2020) analyzed Indonesian consumer goods businesses, finding diversification, financial leverage, and company size positively impacted profitability. Product diversification, financial leverage, and firm size had a substantial positive impact, while capital structure and firm size had a negative effect.

Angels et al (2020) investigated the conversion of tacit and explicit knowledge into profit, emphasizing product innovation and customer centricity. Structural equation modeling on 153 industrial organizations showed both knowledge types contribute to lasting competitive advantages and profitability through consumer simplicity and product improvements.

Sureerat et al (2015) explored the link between proactive product strategy and marketing profitability in Thai home furnishings retailers. Their regression analysis indicated a proactive product strategy, including new product development and original design, significantly enhances marketing advantage and positively impacts the bottom line.

Rarry and Nizar (2002) studied the impact of product-market expansion on profits in the financial services industry, focusing on credit unions. They found that market expansion significantly influenced profitability, while the impact of product growth was minimal. Businesses prioritizing new markets outperformed those focusing on product, current markets, or a mix of both..

Effects of Promotion Strategy on Sales Volume

Adamu (2020) found in Benue State that pricing positively influences small and medium-sized enterprises’ (SMEs) performance, branding shows a significant positive impact, and promotion has no significant effect. Adamu (2020) discovered that market penetration, product packaging, and price positively affect SMEs in Makurdi Metropolis. Eniola and Olorunleke (2020) suggest that product, promotion, packaging, and price significantly impact SMEs’ performance in Kwara State. Mahabub et al. (2020) revealed that cross-border mergers and acquisitions positively influence acquiring firms’ marketing capabilities and overall performance. Sande and Tabitha (2020) found that implementing a market penetration strategy positively affects public universities’ performance in Kenya. Abdulrahmam et al. (2019) concluded that triadic strategic alignment positively correlates with firm performance. Asaad et al. (2018) showed that higher education marketing, social media marketing, pricing, promotions, place, and after-sales service strategies collectively significantly impact organizational performance.

Effects of Price Strategy on Market Share.

Xhia-Huei et al. (2020) found that selling counterfeit products with deceptive signals initially boosts e-commerce sales but leads to long-term losses. Wangui and Kiragu (2018) discovered a positive relationship between pricing and hotel growth, recommending regular price reviews with quality maintenance. Dudu (2018) observed that competitors’ pricing affects consumers’ purchasing behavior, emphasizing firms’ communication of value through pricing strategies. Seonir et al. (2017) concluded that implementing a value-based pricing strategy and higher price levels positively influence profitability, while lower price levels have a negative impact on financial performance in Brazilian enterprises.

Effect of Place Strategy on Customer Loyalty

Bernd and Alexandra (2021) explore the impact of credible location brands on consumer attachment, word-of-mouth, and intention to return. Yeyi et al. (2020) investigate emotional place attachment influenced by feelings toward international companies, emphasizing brand loyalty’s role in place attachment. Sudari et al. (2019) analyze the marketing mix’s effect on customer loyalty in Malaysian food and beverage SMEs, highlighting the impact of product, promotion, place, and pricing on customer satisfaction and loyalty. Praveen and Tanui (2015) examine the impact of store location, product variety, and quality on customer loyalty in fruit and vegetable stores, finding a significant relationship between store location, product quality, and consumer loyalty. Michael et al. (2012) study the effects of place attachment on brand loyalty in Ghanaian restaurants, revealing that identity-based and emotion-based attachment positively impact brand loyalty. Alexandris et al. (2006) investigate the contribution of place attachment and service quality to increasing customer loyalty in a skiing resort, finding a significant influence of place attachment on skiers’ loyalty.

METHODOLOGY

Research Design

The research objectives were accomplished through a survey research design, aligning with the recommendation of Iheanacho & Iheanacho (2012) for studying the impact of marketing strategies on North-Central Nigerian agribusinesses. Utilizing systematic data collection methods such as well-structured questionnaires or interviews, this approach spans wide geographical areas, offering a comprehensive understanding of marketing practices and performance factors within the agribusiness sector. The study focuses on the North Central region, a geopolitical zone in Nigeria, comprising six states—Benue, Kwara, Niger, Kogi, Nasarawa, and Niger. With diverse religious practices and a strategic geographical location surrounded by the River Niger and the River Benue, the region has an estimated population of 29,715,722 people as of 2021, based on the National Population Commission’s forecast. The population of the study consists of 2,216 registered agribusinesses from Benue, Nassarawa, Kogi, Kwara, and Niger States, particularly those involved in granulated cassava processing and registered members of their Local Unions/Associations, SMEDAN, and State Ministry of Commerce, Trade, Industry, and Investment.

For sampling, a multi-stage approach was employed. In the first stage, five states—Benue, Nassarawa, Kwara, Kogi, and Niger—were purposively selected based on the intensity of granulated cassava processing activities. In the second stage, stratified random sampling was applied, considering the number of granulated cassava processing agribusinesses after stratification of the population using registers of members kept by Unions/Association Leaders, SMEDAN, and the Ministry of Commerce, Trade, Industry, and Investment. The questionnaire was administered to 338 respondents across the five selected states. Taro Yamane Formula was employed to arrive at the sample size of 332 (from the total population of 2,216 agribusinesses).

Statistical formula devised by Taro Yamane is as follows:

Where;

n = Sample Size

N = Finite

e = Level of Significance(or limit of tolerance error)

1 = Unity (i.e constant)

The sample size for cassava granules processing agribusinesses is determined as follows:

= 338

Thus, these respondents were randomly selected in the states under study. The Bohley’s proportional formula was used for allocating the samples to different states as follows: 80 respondents from Benue State, 70 from Nassarawa State, 75 from Kogi State, 65 from Kwara and 48 from Niger State.

Instrument of Data Collection

Primary data was used for the study. The primary data was obtained through the use of well-structured and self-administered questionnaire on the respondents based on the objectives of the study.

Validity of the Instrument

The data obtained from any research instrument, be it a questionnaire or a test, should address the research questions and hypotheses when conducting research. When repeated under similar conditions, the data obtained in such a situation should produce results that are comparable. Consequently, a valid instrument measures what it is designed to measure. Validity is the capacity of a measuring instrument to generate trustworthy results and measure what it purports to measure. The primary objective of the pilot study in this study is to collect data from a subset of the target population in order to assess the instrument’s validity and reliability for use in a larger-scale investigation. [If there is no list of SMEs, pilot test role of establishing the population must be included here] This study took into account the two predominant forms of validity, namely content validity and construct validity. Content validity was assessed by obtaining input from supervisors and experts in the study field. Construct validity was evaluated using a factor analysis tool, which included statistical measures such as the Kaiser-Meyer-Olkin (KMO) test and Barlett’s Test of Sphericity among others. In order to ascertain the reliability and effectiveness of the instrument, a pilot study was conducted using a subset of the overall sample which is one third of the total sample of 338. This involved administering 101 questionnaires to a specific group of respondents within the study area. The findings from this pilot study were then utilized to perform exploratory factor analysis, as presented in Tables 1-4.

Table 1: Kaiser-Meyer-Olkin and Bartlett’s Test

Source: Author’s Computation Using SPSS 26.0

Following the completion of the pilot test, the input variables utilized in this study were submitted to explanatory factor analysis. The purpose of this analysis was to examine the alignment between the constructs as defined in the literature and the components generated from the factor analysis. In the realm of evaluating the soundness of a research instrument, the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity are frequently employed as means of appraising the appropriateness of data for factor analysis, a critical component of establishing construct validity.

The dataset exhibits strong suitability for factor analysis as indicated by the high Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy, with a value of .957, close to the optimal score of 1. This signifies a robust adequacy of sampling for factor analysis, implying substantial common variance among variables—critical for factor analysis and thereby construct validity assessment. Additionally, Bartlett’s Test of Sphericity validates the presence of relationships among variables, confirming that the correlation matrix significantly deviates from an identity matrix, substantiating the appropriateness of factor analysis on the dataset (Approx. Chi-Square = 31.848, df = 28, Sig. = .028).

Table 2: Communalities

Extraction Method: Principal Component Analysis

Legend: PRF = Profit, SLV = Sales Volume, MKS = Market Share, CUL = Customer Loyalty, PDS =Product Strategy, PRS = Promotion Strategy, PST = Price Strategy, PLS = Place Strategy

Source: Author’s Computation Using SPSS 26.0

Table 2 show the result of communalities for the variables of the study. In the context of the validity of a research instrument, the provided table shows communalities for each variable before and after the extraction of factors using Principal Component Analysis (PCA). Prior to factor extraction, communalities (which indicate the fraction of each variable’s variation that is explained by the retrieved factors) are all set to 1.000, suggesting that the variables can be fully explained by themselves. The extraction communalities are the communalities after extracting the factors using PCA. The values range from .679 to .920. A higher value indicates that a larger proportion of the variable’s variance is explained by the extracted factors. The extraction communalities, ranging from .679 to .920, demonstrate that the extracted factors (or components) explain a substantial proportion of the variance in the respective variables. This suggests that the factors extracted through PCA are meaningful and relevant to the observed variables. Variables like Profit (PRF), Sales Volume (SLV), and Place Strategy (PLS) have relatively high extraction communalities (above .9), indicating that the extracted factors explain a significant portion of the variance in these variables. This supports the construct validity of the research instrument since these variables are well-represented by the extracted factors. On the other hand, variables like Customer Loyalty (CUL), Product Strategy (PDS), and Promotion Strategy (PRS) have slightly lower extraction communalities (around .68 to .72), suggesting that the extracted factors explain a moderate portion of the variance in these variables. Further exploration or refinement of the instrument may be needed to improve the representation of these variables by the extracted factors, enhancing construct validity. The result of communalities provide insight into the extent to which the extracted factors explain the variance in each variable, thus aiding in the assessment of the research instrument’s construct validity. Higher extraction communalities generally indicate a better representation of the variables by the extracted factors, supporting the validity of the instrument.

Table 3: Total Variance Explained

Legend: PRF = Profit, SLV = Sales Volume, MKS = Market Share, CUL = Customer Loyalty, PDS =Product Strategy, PRS = Promotion Strategy, PST = Price Strategy, PLS = Place Strategy

Source: Author’s Computation Using SPSS 26.0

Table 3 shows the percentage of variance explained by each component in a principal component analysis (PCA), which is useful information when assessing the reliability of a research instrument. Important measures of construct validity shown by the analysis include the initial eigenvalues, sums of squared loadings from extraction, and sums of squared loadings from rotation for each component. The cumulative percentage of variance explained by the components is fundamental for construct validity assessment. The first four components collectively explain approximately 77.778% of the total variance. This indicates that these components effectively represent a large portion of the underlying variance in the observed variables, affirming the construct validity of the research instrument. Retaining all components is important to comprehensively capture this variance. The eigenvalues represent the variance explained by each component. Higher eigenvalues indicate a greater amount of variance explained. The first component has the highest eigenvalue, explaining 31.189% of the variance. Each component, therefore, contributes uniquely to explaining the variability in the data, underlining the necessity of retaining all components for a comprehensive understanding of the construct.

The cumulative percentage of variance explained showcases the aggregate contribution of each component. It is essential to retain all components to capture a progressively increasing amount of variance and construct representation. This comprehensive understanding is vital for construct validity evaluation, as each component contributes uniquely to the overall variance and understanding of the constructs. As stated earlier, retaining all components is crucial for construct validity assessment, ensuring that a significant proportion of the variance is accounted for, and each component uniquely contributes to the understanding of the constructs. It allows for a more comprehensive and accurate representation of the underlying construct relationships, enhancing the validity of the research instrument.

Figure 5: Scree Plot

The scree plot is a visual tool employed in Principal Component Analysis (PCA) to aid in the identification of the optimal number of components to maintain. It displays the eigenvalues (variance explained) associated with each component, allowing us to identify the point of diminishing returns, indicating where additional components contribute less to the total variance. In this scree plot, the x-axis represents the components (in this case, from 1 to 8), and the y-axis represents the eigenvalues or the percentage of variance explained. For each component, the corresponding eigenvalue is plotted. The first component has the highest eigenvalue, explaining the largest proportion of variance. The second component has the next highest eigenvalue. The third component follows while the fourth component has eigenvalue of 12.951%. While traversing the various components, a discernible trend of decreasing eigenvalues becomes apparent. The scree plot would depict a pronounced decrease in eigenvalues in the beginning, followed by a stabilization beyond a specific threshold. In the present scenario, the plot would exhibit a discernible juncture at which the eigenvalues begin to stabilize, signifying that supplementary components beyond said juncture make a comparatively lesser contribution to the explained variance.

Reliability of Instrument

Table 4: Reliability Statistics

Source: Author’s Computation using SPSS 26.0

The reliability of the instrument utilized in the investigation is presented in Table 4. The Cronbach’s Alpha reliability statistics presented evaluate the internal consistency and reliability of each variable in the study, as well as an overall reliability score. Within the framework of Cronbach’s Alpha statistics, the presented table showcases the reliability analysis pertaining to each variable contained within the research instrument, as well as the overall reliability of said instrument. The establishment of a research instrument’s reliability is crucial in guaranteeing the consistency and accuracy of the obtained data. The present study utilized Cronbach’s Alpha, a commonly adopted metric for evaluating internal consistency, to gauge the reliability of the research instrument across several factors. The variable “Profit (PRF)” demonstrated a significant degree of internal consistency, as indicated by a Cronbach’s Alpha coefficient of 0.851. This finding suggests a high level of dependability in the measurement of profit-related variables within the dataset. In a similar vein, the construct of “Sales Volume (SLV)” had favorable internal consistency, as evidenced by a Cronbach’s Alpha coefficient of 0.820. This suggests that the measurement of sales volume can be considered reliable.

The metric known as Market Share (MKS) had a notable degree of internal consistency, as evidenced by a Cronbach’s Alpha coefficient of 0.863. This implies that the variable consistently measured multiple factors associated with market share. In contrast, the construct of “Customer Loyalty (CUL)” demonstrated satisfactory internal consistency, as evidenced by a Cronbach’s Alpha coefficient of 0.795. This coefficient suggests that the measurement of customer loyalty is reasonably reliable. The variables associated with strategic aspects, including “Product Strategy (PDS),” “Promotion Strategy (PRS),” “Price Strategy (PST),” and “Place Strategy (PLS),” exhibited satisfactory to strong levels of internal consistency. The Cronbach’s Alpha scores for each construct were 0.831, 0.850, 0.823, and 0.827, indicating that the measurement of each construct was reliable.

The study instrument’s overall dependability, determined by averaging the Cronbach’s Alpha values for all variables, is 0.833. This finding suggests a strong internal consistency or reliability of the research instrument as a whole. The Cronbach’s Alpha statistics indicate that each variable in the study instrument exhibits a satisfactory level of internal consistency or reliability, ranging from good to high. The instrument demonstrates a high level of overall reliability, indicating that the variables collectively offer a dependable measure of their separate constructs within the study’s context.

Method of Data Collection

The study utilized primary data. These data were collected using structured questionnaire. Enumerators were employed for data collection in each of the state. A well-structured and self-administered questionnaire containing closed questions was developed and used for gathering data from agribusinesses that are into cassava granules processing. The questionnaire contains three sections, Section A contain Demographic profile of agribusinesses; Section B contain questions of marketing strategies persuasiveness, while Section C contain questions on performance indicators of agribusinesses and section D contain questions on constraint affecting performance .

Variable specification/Model specification

Variable Specification.

Dependent Variable (Performance).

The dependent variable in the study is performance. In order to analyze the effect of marketing strategies on the agribusiness performance, profit, sales volume, market share, and customer loyalty were used as proxy for agribusinesses performance.

1. Profit: This will be measured in Naira.
2. Sales Volume: This was measured using quantity of units sold in kilograms by the respondents.
3. Market Share: Was measured in percentage
4. Customer Loyalty: This will be measured using a 4 point Likert Scale

Independent Variables.

With regards to the independent variables, marketing strategies in this study will focus on four important components, product Strategy, Promotion Strategy, Price Strategy, and Place Strategy.

1. Product Strategy: This was measured using a 4 point Likert Scale
2. Promotion Strategy: This was measured using a 4 point Likert Scale
3. Price Strategy: This was measured using a 4 point Likert Scale
4. Place Strategy: This was measured using a 4 point Likert Scale

Model Specification.

In this study, multivariate regressions analysis will be used to measure the effect independent variables have on dependent variable. This model is adopted from the work of Cross (2018) where he used just two variables(sales volume and market share) to measure the dependent variable. This study modifies the model by using more than two variables to measure performance. The model is specified below;

 Model in implicit form:

PRF = f (PDS)………………………………………………… (4)

SLV = f (PRS)………………………………………………… (4)

MKS = f(PST) …………………………………………….…..(5)

CUL = f(PLS) …………………………………………………(6)

Where,

PRF = Profit (Naira).

SLV = Sales Volume(quantity of units sold in Kilograms).

MKS =Market Share (Percentage).

CUL = Customer Loyalty (4 Point Likert Scale)

PDS = Product Strategy (4 Point Likert scale)

PRS = Promotion Strategy (4 Point Likert Scale)

PST = Price Strategy (4 Point Likert Scale)

PLS = Place Strategy (4 Point Likert Scale)

Model in explicit form:

PRF = b_o+b₁PDS+U_t……………………………..…. (7)

SLV = α_o+α₁PRS+E_t……………………………..….. (8)

MKS = γ_o+γ₁PRS+_t……………………………..….. (9)

CUL = β_o+β₁PRS+V_t……………………………….. (10)

Where,

b_o, α_o, γ_o, β_o = Regression constant.

b₁, α_1, γ_1,β₁= Regression coefficient.

U_t, E_t, ε_t, V_t = Stochastic error term.

A priori expectation

b₁>0; α₁>0; γ₁>0; β₁>0.

Method of analytical technique

The study adopted the use of multivariate regressions analysis as basis for data analysis. Multivariate simple linear regressions analysis was used to achieve objective one, two, three and four and hypotheses one, two, three and four. Descriptive statistics was used to achieve objective five while structural equation model was used to achieve objective six of the study. In diagnostic testing, various statistical methods are employed to assess key assumptions and identify potential issues. The Kolmogorov-Smirnov (KS) and Shapiro-Wilk (SW) tests evaluate normality assumptions by comparing sample data to a normal distribution. A correlation matrix reveals the strength and nature of relationships between variables, aiding in the detection of multicollinearity, where highly correlated independent variables may impact model stability and interpretability. Mahalanobis distance is utilized for multivariate analysis, considering correlations and variances to measure the distance between a point and a distribution. Cook’s distance, a diagnostic measure in regression analysis, assesses the influence of individual data points on regression coefficients and overall model fit, helping identify influential observations that may significantly impact the model’s predictions and parameter estimates.

RESULTS AND DISCUSSIONS

Regression Results and Discussion

Before the presentation and discussion of the regression analysis, some diagnostic tests such as normality of the data and multicollinearity used for the analysis of this study are examined as shown below. The data normality is graphically presented as shown in figure 3 and 4.

Table 11: Tests of Normality: Kolmogorov-Smirnov and Shapiro-Wilk

Legend: PRF = Profit, SLV = Sales Volume, MKS = Market Share, CUL = Customer Loyalty, PDS =Product Strategy,PRS = Promotion Strategy, PST = Price Strategy, PLS = Place Strategy

 Source: Author’s Computation, using SPSS 26.0 2023

Table 11 shows the Kolmogorov-Smirnov test and the Shapiro-Wilk test are two statistical tests used to test the hypothesis that a data set comes from a normal distribution. The Kolmogorov-Smirnov test compares the empirical distribution function of the data set to the theoretical distribution function of the normal distribution. The Shapiro-Wilk test is a more powerful test than the Kolmogorov-Smirnov test, but it is also more sensitive to departures from normality. The null hypothesis is that the data sets for PRF, SLV, MKS, PDS,PRS, PST and PLS come from a normal distribution. The alternative hypothesis is that the data sets do not come from a normal distribution. The significance level for both the Kolmogorov-Smirnov test and the Shapiro-Wilk test is 0.05. This means that we can reject the null hypothesis if the p-value is less than 0.05. The p-values for the Kolmogorov-Smirnov test are 0.564, 0.265, 0.185, 0.168, 0.996, 0.589, 0.897 and 0.092. All of these p-values are greater than 0.05, so we cannot reject the null hypothesis for any of the data sets. The p-values for the Shapiro-Wilk test are 0.229, 0.774, 0.166, 0.168, 0.422, 0.449, 0.263 and 0.360. The significance level for all the variables are greater than the critical value of 0.05, hence we cannot reject the null hypothesis.

Based on the results of the Kolmogorov-Smirnov test and the Shapiro-Wilk test, we cannot reject the null hypothesis for the PRF, SLV, MKS, PDS,PRS, PST and PLS data sets. This means that the all the data set come from a normal distribution. This inference is important in modeling meaningful empirical results. The implications of the results are that the assumptions of normality are met. The normality assumption is important because it ensures that the errors are normally distributed. If the errors are not normally distributed, then the standard errors of the regression coefficients may be unreliable and the t-statistics may be biased.

Figure 7: Regression Standardized Residual for Models I, II, III, IV

Figure 7:Regression Standardized Residual for Models I, II, III, IV

Source: Author’s Computation, using SPSS 26.0 2023

The histogram in Figure 7 displays the residuals, accompanied by an overlaid normal curve. The residuals exhibit a near approximation to normality, suggesting that the underlying data follows a normal distribution. The provided figure displays the relationship between the residuals and the dependent variables of Profit (PRF), Sales Volume (SLV), Market Share (MKS), and Customer Loyalty (CUL) within the study area. The observed pattern depicted above suggests that there are no apparent issues with the assumption that the residuals follow a normal distribution for each level of the dependent variable, and that the variance of the residuals remains consistent across different levels of the dependent variable. The probability of observing a histogram of sample data that exhibits a completely smooth normal curve is quite improbable, particularly when the sample size is tiny. The assumption of normality is satisfied when the data exhibits an approximate normal distribution, characterized by a central peak and symmetrical properties. This observation is consistent with the results obtained from the Kolmogorov-Smirnov test and the Shapiro-Wilk test for assessing normality.

Table 12: Correlation Matrix: Test for Multicollinearity

PDS =Product Strategy, PRS = Promotion Strategy, PST = Price Strategy, PLS = Place Strategy

Source: Author’s Computation, using SPSS 26.0 2023

As shown in Table 12, in the context of multicollinearity, a correlation matrix can help identify potential multicollinearity issues among the predictor variables. The correlation matrix assesses multicollinearity among four predictor variables: Product Strategy (PDS), Promotion Strategy (PRS), Price Strategy (PST), and Place Strategy (PLS). Multicollinearity refers to the presence of high correlations between predictor variables in a regression model, and it can have important implications for the interpretation and stability of regression results. The correlation analysis reveals relationships between Product Strategy (PDS) and various marketing strategies. The correlation coefficient of 0.349 between Product Strategy (PDS) and Promotion Strategy (PRS) indicates a moderate positive correlation, suggesting some connection between these two variables. While not extremely high, this correlation may raise concerns about multicollinearity in a larger dataset. Conversely, the correlation coefficients of 0.256 with Price Strategy (PST) and 0.263 with Place Strategy (PLS) show weak positive correlations, indicating only slight tendencies for Product Strategy to move in the same direction as Price and Place Strategies. Currently, multicollinearity is not a significant issue between Product Strategy and Price or Place Strategy.

PRS and PST, PRS and PLS, PST and PLS

The correlations between Promotion Strategy (PRS) and Price Strategy (PST), Promotion Strategy (PRS) and Place Strategy (PLS), as well as between Price Strategy (PST) and Place Strategy (PLS), are relatively low, indicating that these variables are not strongly correlated with each other. Multicollinearity does not appear to be a major issue among these pairs of variables. Based on the correlation matrix provided, there is some evidence of correlation between certain pairs of predictor variables, but none of the correlations are extremely high. Multicollinearity does not seem to be a severe concern in this dataset. However, it is important to note that the impact of multicollinearity also depends on the sample size and the specific research context, so further investigation may be warranted in a larger or different dataset.

Table 13: Statistical Significance of Model ANOVA^a

Source: Author’s Computation, using SPSS 26.0 2023

The analysis employs multiple regression to understand the relationship between common predictors (PDS, PRS, PST, PLS) and distinct dependent variables (PRF, SLV, MKS, CUL). The first regression concerning Profit (PRF) indicates a statistically significant relationship (F = 5.073), (p < 0.001) between the predictors and PRF, elucidating that PDS, PRS, PST, and PLS collectively explain a notable portion of the variance in Profit. The regression sum of squares reveals that the model accounts for (179.817) units of variance in PRF, emphasizing the average amount of variance explained by the predictors. Moving to Sales Volume (SLV), the regression model also emerges as statistically significant (F = 3.350), (p = 0.002), showcasing the combined impact of PDS, PRS, PST, and PLS in delineating a substantial portion of the variance in SLV.

Specifically, the model illuminates (359.133\) units of the variance in SLV, underscoring the average variance explained by the predictors. For Market Share (MKS), the regression model is marginally significant (F = 2.052), (p = 0.046), implying that PDS, PRS, PST, and PLS somewhat contribute to the variance in MKS. Although the significance is marginal, the model illuminates (238.596) units of variance in MKS, denoting the average variance explained by the predictors. Lastly, regarding Customer Loyalty (CUL), the regression model proves to be statistically significant (F = 2.324), (p = 0.007), signifying that the amalgamation of PDS, PRS, PST, and PLS substantially expounds upon the variance in CUL. The regression model elucidates (154.688) units of variance in CUL, reiterating the average variance explained by the predictors for this particular dependent variable.

In summary, the regression analyses affirm that the predictors (PDS, PRS, PST, PLS) exert varying degrees of influence in explaining the variance within the respective dependent variables (PRF, SLV, MKS, CUL). The models for Profit (PRF), Sales Volume (SLV), and Customer Loyalty (CUL) are statistically significant, suggesting a meaningful and substantial relationship between the predictors and these variables. Notably, the model for Market Share (MKS), while marginally significant, still contributes to the explanatory power, offering insights into the relationship between the predictors and MKS. These findings collectively provide valuable insights into how these key predictors impact different aspects of the business, guiding potential strategies and decisions.

Table 14: Model Summary

Source: Author’s Computation, using SPSS 26.0 2023

Table 14 presents the model summary for four different regression models, each evaluated based on several key metrics:

The results of the regression models reveal distinct patterns. In Model 1, a substantial correlation coefficient (R) of 0.752 denotes a moderately strong positive linear association between the predictor(s) and the dependent variable. The high coefficient of determination (R²) at 0.566 indicates that approximately 56.6% of the variance in the dependent variable can be reliably predicted by the chosen predictors. The adjusted R2, factoring in the number of predictors, stands at 0.455. Additionally, the estimated standard error of 3.04008 signifies the average discrepancy or variation between observed and projected values, providing valuable information about the model’s accuracy. Transitioning to Model 2, the correlation coefficient (R) remains substantial at 0.636, suggesting a modest positive linear association. The R² of 0.404 indicates that around 40.4% of the variability in the dependent variable can be explained by the predictors. The adjusted R², accounting for the number of predictors, is 0.372. The estimated standard error of 4.39486 reflects the average discrepancy between observed and anticipated values, offering insights into the model’s precision.

In Model 3, a correlation coefficient (R) of 0.587 implies a moderately strong positive linear association. The R² of 0.345 indicates that approximately 34.5% of the variability in the dependent variable is accounted for by the predictors, with an adjusted R2 of 0.252 considering the penalty for the number of predictors. The estimated standard error is 2.75669, representing the average discrepancy between predicted and actual values. Finally, for Model 4, a correlation coefficient (R) of 0.646 suggests a moderately strong positive linear association, with an R² of 0.417 indicating that approximately 41.7% of the variability in the dependent variable can be attributed to the predictors. The adjusted R² of 0.397 and an estimated standard error of 4.12693 provide a comprehensive assessment of the model’s explanatory power and precision.

Table 15: Coefficients^a

Legend: PRF = Profit, SLV = Sales Volume, MKS = Market Share, CUL = Customer Loyalty, PDS =Product Strategy,PRS = Promotion Strategy, PST = Price Strategy, PLS = Place Strategy

Source: Author’s Computation, using SPSS 26.0 2023

 Effect of product strategy on the level of profit of agribusinesses in North-   Central Nigeria

The study delved into understanding the connection between Product Strategy (PDS) and Profit (PRF) within the agribusiness landscape of North-Central Nigeria. The outcomes reveal a subtle negative correlation between product strategy and profit, implying that in this specific context, the influence of product strategy on overall profitability might be relatively minor. Moreover, the absence of statistical significance underlines that the observed relationship between product strategy and profit could plausibly be a result of chance rather than a definitive and measurable cause-and-effect dynamic. This finding is contrary to research conducted by Destya et al (2020) who examined the impact of diversification strategies, financial leverage, firm size, and capital structure on the profitability of consumer products companies listed on the Indonesia stock exchange. The findings of this study yielded comparable outcomes. Based on the findings of the panel analysis, it can be concluded that the factors of product strategy, product diversification strategy, and financial leverage exhibit a positive and statistically significant impact on profitability. Emma (2016), Sureerat, Prthanporn and Sutana (2015), Angels et al (2020), Sureerat, Prthanporn, and Sutana (2015), Rarry and Nizar (2002) among others found a positive relationship. However, from a practical standpoint, this brings to light the realization that exclusive reliance on refining product strategy may not yield significant enhancements in the profitability of agribusinesses situated in North-Central Nigeria. It raises the need for a diversified approach, where agribusinesses should consider exploring other strategic avenues such as marketing, cost management, and operational efficiency to potentially achieve more substantial improvements in their bottom line. Additionally, future studies in this domain should broaden their scope by incorporating a wider array of variables and examining a more diverse set of agribusinesses. This broader approach would shed more light on the intricate dynamics that influence profitability within this specific geographical region.

Effect of promotion strategy on sales volume of agribusinesses in North-Central Nigeria

In this study, the evaluation of the Sales Volume (SLV) model revealed a strong and significant negative impact of Promotion Strategy (PRS) on sales volume in agribusinesses within North-Central Nigeria. The negative standardized coefficient (Beta) for the promotion strategy suggests that an increase in emphasis on this strategy is associated with a decrease in sales volume. The statistical significance of this relationship, supported by the low p-value and the significant t-value, emphasizes the robustness of this finding. It signifies that promoting products within the agribusiness sector may not necessarily translate to increased sales volume in this specific geographical context. This finding is in tandem with that of Adamu (2020a) who investigated how marketing strategies affected the success of small and medium-sized enterprises and found that the relationship between Promotion (PRO) and Performance of Small and Medium Scale Enterprises in Benue State (PFM) exhibited an undesirable correlation. In a another study, Adamu (2020b) equally found a negative association between promotion and performance of SMEs in Makurdi metropolis, Benue State. However, the studies conducted by Asaad et al. (2018), Eniola and Olorunleke (2020), Mahabubur et.al (2020), Abdulrahmam et al. (2019), Sande and Tabitha (2020) indicated that promotion strategy has a positive and significant effect on business performance. The negative effect of promotion strategy on agribusiness performance could be due to several reasons beyond the scope of this study. As a result, further studies is needed to unravel the reasons for the negative relationship.

However, the absence of multicollinearity issues, as indicated by the collinearity statistics, strengthens the reliability of the estimated coefficients and the overall dependability of the model. This suggests that the observed negative impact of promotion strategy on sales volume is not confounded by collinearity-related distortions. Consequently, it becomes imperative to carefully evaluate and strategize the role of promotion in the marketing efforts of agribusinesses in North-Central Nigeria, considering its potential influence on sales volume. Future research in this domain could further unravel the complex dynamics of promotion strategy within this region and shed light on specific factors contributing to the observed negative impact on sales volume.

Effect of price strategy on market share of agribusinesses in North-Central   Nigeria

The study explored the dynamics between Price Strategy (PST) and Market Share (MKS) in the context of agribusinesses in North-Central Nigeria. The findings underscore a notable positive effect of the price strategy on market share. This suggests that an emphasis on strategic pricing within the agribusiness sector is linked to an increase in market share. The statistical significance of this relationship, indicated by a low p-value and a significant t – value, further validates this positive influence and emphasizes its importance in shaping market dynamics. This finding is in tandem with those of Gbolagade et.al (2013) who studied the effect of marketing strategy on company performance prices is a strong predictors of business performance in terms of profitability, market share. Other researcher who found similar positive correlation include; Wangui and Kiragu (2018), Dudu (2018), Seonir et al (2017). Their findings add credence to the finding of the current study. However, Xhia-Huei et al (2020) carried out empirical study on scales performance effect and pricing strategy for E-commerce found a contrary result. Their findings indicate that engaging in the sale of counterfeit products using deceptive signals leads to an initial improvement in sales performance. However, this practice ultimately results in a subsequent loss in long-term performance thereby leading to a negative effect of pricing strategy on performance.

From the statistical perspective, the absence of multicollinearity problems, as evidenced by the collinearity statistics, provides assurance regarding the stability and reliability of the estimated coefficients within the model. This highlights that the observed positive effect of the price strategy on market share is not distorted or confounded by collinearities among the variables. Consequently, agribusinesses in North-Central Nigeria may need to carefully consider their pricing strategies, aiming to leverage this positive influence to gain a more significant share in the market. Future research could delve deeper into understanding the specific aspects of pricing strategies that drive this positive impact and explore how agribusinesses can optimize their pricing decisions to maximize market share effectively.

Effect of place strategy on level of customer loyalty of agribusinesses in North- Central Nigeria

The objective of this study was to examine the correlation between Place Strategy (PLS) and Customer Loyalty (CUL) within the agribusiness sector situated in North-Central Nigeria. The findings illuminate a noteworthy positive impact of place strategy on customer loyalty. This implies that strategic considerations related to product placement, distribution channels, and ease of access significantly impact consumer loyalty in the agribusiness sector. The statistical significance of the observed link is reinforced by a low p-value and a substantial t-value, indicating the strength and importance of this result in influencing the dynamics of customer loyalty. This finding is in line with those of Bernd and Alexandra (2021) who investigated brand credibility place attachment, and consumer loyalty. The researchers found that place brand plays a significant role in shaping consumers’ connection towards the location and improving on loyalty. Also, researchers such as Yeyi, et al (2020), Sudari et al (2019), Praveen, and Tanui (2015), Michael et al (2012), Alexandris et al (2006) among others found positive and significant association between place strategy and customer loyalty.

Additionally, the presence of low multicollinearity, as evidenced by the collinearity statistics, enhances the robustness and dependability of the calculated coefficients in the model. This indicates that the observed beneficial influence of the location strategy on customer loyalty stays unchanged by any collinear relationships among the variables. Hence, it is recommended that agribusiness enterprises operating within the North-Central region of Nigeria should strategically enhance their location plan to enhance customer loyalty. Subsequent investigations should prioritize the acquisition of a more profound comprehension regarding the specific constituents of location strategy that contribute to the advantageous influence mentioned. Additionally, there is a need to explore how agribusinesses might tailor their approaches to optimize consumer loyalty inside this specific geographical area.

This study offers significant insights into the impact of place strategy on customer loyalty within the agricultural sector in North-Central Nigeria. The observed beneficial impact indicates the significance of strategic positioning and ease of access in cultivating client loyalty within the agribusiness industry. The exclusion of multicollinearity issues enhances the validity of this association, underscoring the imperative for agribusinesses to meticulously evaluate and optimize their placement tactics in order to augment consumer loyalty. Overall, the four models suggest that different marketing strategies (PDS, PRS, PST, PLS) have varying impacts on the study variables. Specifically, promotion and place strategies significantly affect sales volume and customer loyalty, respectively, while price strategy strongly influences market share. However, product strategy shows a negligible effect on profit. Fortunately, all models indicate no multicollinearity concerns, affirming the reliability of the estimated coefficients.

CONCLUSION AND RECOMMENDATIONS

Conclusion

In conclusion, this study has shed light on the intricate relationship between marketing strategies and the performance of agribusinesses in North-Central Nigeria. The results, derived from four distinct models, have revealed that these marketing strategies, namely product strategy, promotion strategy, price strategy, and place strategy, exert diverse influences on the key performance indicators of profit, sales volume, market share, and customer loyalty. Notably, promotion strategy emerges as a potent driver of sales volume, underlining the significance of promotional activities in driving consumer engagement and boosting sales from both regression model and the structural equation model. Likewise, place strategy stands out as a critical factor in fostering customer loyalty, emphasizing the importance of strategic distribution and accessibility. Furthermore, price strategy emerges as a key determinant of market share, highlighting the role of competitive pricing in gaining a larger market presence. However, it is essential to note that the impact of product strategy on profit appears to be minimal, suggesting that other factors may play a more prominent role in profit generation within the agribusiness context. The result of the structural equation model indicates that persuasiveness as a mediating variable has a positive and significant effect on agribusiness performance in North Central Nigeria. This is consistent with the results of direct, indirect and total effects of the structural equation model. These insights provide valuable guidance to agribusiness practitioners in North-Central Nigeria, helping them make informed decisions to optimize their marketing strategies and ultimately enhance their performance in this vital sector.

Recommendations

Based on the specific results of the study on the effect of marketing strategies on agribusiness performance in North-Central Nigeria, the following recommendations are made:

1. It is recommended that further market research and product development should be pursued due to its insignificant effect on profit. Diversifying and increasing product strategy can boost profits by understanding market needs and adjusting products to them.
2. Agropreneurs in the study area should reconsider their promotional tactics as a result of its negative effect on sales volume. Crafting promotions that highlight the unique value propositions of products could help in turning this negative impact into a positive one, ultimately boosting sales volume.
3. Agribusinesses should conduct a thorough market analysis to set optimal prices that attract customers while ensuring profitability. Dynamic pricing strategies, seasonal discounts, and value-based pricing could be considered to further enhance market share.
4. Optimizing the distribution channels and locations is recommended as it will enhance the accessibility and convenience for customers by fostering strong relationships with local distributors and retailers which can further solidify the positive impact of place strategy on customer loyalty.

 Contributions to Knowledge

This study significantly contributes to the understanding of how marketing strategies influence the performance of agribusinesses in the North-Central region of Nigeria. By investigating the effects of specific marketing strategies, including product, promotion, price, and place strategies, on critical performance indicators such as profit, sales volume, market share, and customer loyalty, this research provides valuable insights into the dynamics of the agribusiness sector. The findings, indicating the varying impacts of different strategies on distinct performance metrics, offer actionable knowledge for agribusiness practitioners and policymakers to optimize their marketing approaches.

REFERENCES

1. Abdulrahman, A., Guangming, C., &Yanqind, D. (2019). The Impact of Aligning Business, IT, and Marketing Strategies on Firm Performance. Department of Strategy Management, University of Bedfordshire Business School, Luton LU1 3JU, UK.
2. Achumba, I.C. (2000). Strategic Marketing Management, Mac-Williams and Capital Publisher Inc, Charlotte. U.S.A
3. Achumba, I.C., &Osuagwu, L. (1994). Marketing Fundamentals and Practice, Rock Hill:  USA: AI-Marks Educational Research, Inc.1-40.
4. Adamu, G. (2020). Effect of Market Penetration on the Performance of Small and Medium Scale Enterprises in Makurdi Metropolis, Benue State. International Journal of Business, Management and Social Research, 9(1):508-515
5. Adamu, G. (2020). Effect of Marketing Strategies on the Performance of Small and Medium Scale Enterprises in Benue State. World Journal of Innovation Research, 8(2):59-64
6. Adebisi, S. & Babatunde, B. (2011). Strategic Influence of Promotional Mix on Organizational Sales Turnover in the face of Strong Competitor’s Business. Intelligent Journal, July.Vol.4 No 2pg342-360.
7. Adesoga, D.A. (2015). Promtinal Strategy Impact on Organizational Market Share and Profitability. Acta Universitatis Danubius. Economica, 11(6) 2015.
8.  Agrey, K. K. (2010). Effectiveness of Promotion Mix Methods on Sales in Local pharmaceutical Manufacturing Companies in Kenya. University of Nairoi, Kenya.
9. Akanbi, P. A., &Adeyeye, T.C. (2011) the Association Between Advertising and Sales Volume: A Case Study of Nigerian Bottling Company Plc. Journal of Emerging Trends in Economics and Management Sciences (JETEMS), 2(2):117-123.
10. Akinyele, S.T. (2010) Strategic Management Practice on the Performance of Firms in Nigeria Oil and Gas Industry.  International Journal Research Consumer Management, 1 (4):6-33.
11. Akinyele, S.T. (2011) Strategic Marketing Strategies on the Performance of Firms in Nigeria Oil and Gas Industry. Journal of Emerging Trends in Economics and Management Sciences (JETEMS) 1(1):23-36.
12. Alan, W. M., Marco, H., & Morgan, S. (2015). How Firm Innovativeness and Unexpected Product Reliability Failures Affect Profitability. Journal of Operations Management, 38(8):71-36.
13.  Alexandris, K., Kouthouris, C., & Andreas, M.(2006). Increasing Customers’ Loyalty in a Skiing Resort. International Journal of Contemporary Hospitality Management, 8(5):414-425.
14. Allison, M., & Kaye, J. (2005).‘Strategic Marketing Management for Non-profit Organizations’ A Practical Guide and Work Book. John Wiley Publishers, New York, pg 234-254.
15. Ambler, T., Kokkinaki, F., & Puntoni, S. (2004).Assessing Marketing Performance: Reasons for Metrics Selection. Journal of Marketing Management, (20), 475-498.
16. Amit, R., & Livnat, J. (1988). Diversification Strategies, Business Cycles and Economic Performance. Strategic Management journal.
17. Anas, Y. A., &Batool, A. Q. (2016). Effect of Marketing Strategy on Maximizing the Performance of an Organization. Journal of Academic Research in Business and Social science, vol.6, No4. ISSN: 2222-6990.
18.  Angeles, M.L., Suresh, S., & Paula, V.(2020). The Role of Product Innovation and Customer Centricity in Transformation Tacit and Explicit Knowledge into Profitability. Journal of Knowledge Management, 24(5):1037-1067.
19. Ansoff, H. I. (1965). The New Corporate Strategy. New York: John Wiley.
20. Ansoff, H. I. (1987). Conceptual Underpinnings of Systematic Strategic Management. European Journal of Operational Research.
21. Anurag, S., & Nelson, L. (2004). Linking Product Development Outcomes to Market Valuation of the Firm: The Case of the U.S. Pharmaceutical Industry. Journal of Product Innovation Management.
22. Ardjouman, D., & Asma, B. (2015). Marketing Management Strategies Affecting Performance of Small and Medium Enterprises (SMEs) in Cote d’Ivoire. International Journal of Business and Social Science, 6(4):141-150.
23. Arhar, I., Irfan, H., & Majid, Q. (2012). Impact of Diversification on Firm’s Performance. Journal of American Scientific Research, 4 (80):42-53.
24. Armstrong, S., &Kesten, C. G. (2007).”Competitor-oriented Objectives: The Myth of Market Share”. International Journal of Business, 12 (1):116–134. ISSN 1083-4346.
25. Arvinlucy, A. O. (2012). An Evaluation of Promotional Element Influencing Sales of an Organization: A Case Study of Sales Agricultural and Non-Agricultural Products Among Women Groups, Homa Bay. International Journal of Business and Social Science, 3(5): 2012
26. Asaad, A. K., Ramadan, H. H., Haker, M. R., Daleen, A. J., Cihan, S. M., & Shamal, H. O. (2018). The Effect of Marketing Strategy Implementation on Organization Performance as a Private Business: International Journal of Business Management and Economic Research, 9(3):1281-1295.
27. Banobo, E., & Koroye, B.H. (2011). Sales Promotion Strategies of Financial Institutions in Bayelsa State. Asian Journal of Business Management, 3(3):203-209
28. Barczak, G.(1995). New Product Strategy, Structure, Process, and Performance in the Telecommunications Industry. Journal of Product Innovation Management, 12(12)224-234
29. Baron, R.A. (2008). The role of Affect in the entrepreneurial process. Academy of Management Review, Vol. 33(2): 328 – 340. doi: 10.5465/amr.2008.31193166.
30. Benson, M., Willy, M., & Charles, R. (2015). Effect of Product Development Strategy on Performance in Sugar Industry in Kenya. International Journal of Academic Research in Business and Social Sciences, Vol. 5, No. 12 ISSN: 2222-6990.
31. Bergeron,F.(2002). Strategic Alignment and Business Performance: Operationalizing and testing a co-variation model. Cahier de la Chaire digestion Stratégique des Technologies de l’information, 02(01).
32. Berry, B.W. (1997). Strategic Marketing Work Book for Nonprofit Organizations‟, Chicago Amherst H., Publishers; Wilder Foundation.
33. Billur, A. (2011). New Product Development and New Product Profitability: Exploring the Mediating Role of Speed to Make. Journal of Product Innovation Management, 28(1):63-77.
34. Bitner, M. (2003) Evaluating Service Encounters: The Effects of Physical Surroundings and Employee Reponses. Journal of Marketing, 54(2): 69-82
35. Blotnicky, K. (2009). Examining the Impact of Marketing Orientation on Information Technology adoption in Canadian firms. PhD thesis USA: North-central University. AAT. Retrieved from http://gradworks.umi.com/33/93/3393712.html.
36. Bonnstetter,B.J.(2012. New Research: the Skills that make an Entrepreneur. Haward Business Review, 90,12.Retrieved from https:/thbr.org20/101 2022
37. Bradford, K. (2000).  Simplified Strategic Planning, Chandler House. Retrieved in 2020 from http://www.google/strategy.com.
38. Brost, M. A., &Kleiner, B. H. (1995). New Developments in Corporate Diversification Strategies. Management Research News, 18(3-5), 24-33. http://dx.doi.org/10.1108/ eb028402.
39. Bryson, J. M. (2004). Strategic Planning for Public and Nonprofit Organization’, Jossey- Bass Publishers.
40. Cao, G. (2010). A Four Dimensional View of IT Business value. Systems Research and Behavioral science (formally systems research), 27(3):267-284.
41. Cao, G., Duan, Y., Cadden, T., &Minocha, S. (2016). Systemic Capabilities: The source of IT Business value. Information Technology & People, 29(3):556–579.
42. Caroly, M.C., Gabriel, E.A., & Rodney, ES. (2009). the Effect of Inter-Firm Cost Correlation, IT Investment and Product Decisions and Firm Profitability. Journal of Information System, 23(1):51-78
43. Cataldo, A., McQueen, R. J., &Hardings, J. (2012). Comparing Strategic IT Alignment versus Process IT Alignment in SMEs. Journal of Research and Practice in Information Technology, 44(1):43–57.
44. Central Bank of Nigeria, (2008). The CBN Partnership with Alliance for a Green Revolution in Africa (AGRA): Conceptual Issues, Operation and Prospects for food Security in Nigeria. Economic and Financial Review, Vol.48/4, December, 2008.
45. Central Bank of Nigeria, (2011). Integrating Nigeria’s Agricultural and Financial Value chains: The Role of Nigerian Incentive Based Risk Sharing for Agricultural lending (NIRSAL), November 16, 2011.
46. Cha, H. S., Pingry, D. E., & Thatcher, M. E. (2009). What Determines IT Spending Priorities? Communications of the ACM, 52(8), 105–110.
47. Chan, Y. E. (2000). IT value: The Great Divide between Qualitative and Quantitative and Individual and Organizational Measures. Journal of Management Information Systems, 16(4):225–261.
48. Chan, Y. E., Hu, S. L., Barclay, D. W., & Copeland, D. G. (1997). Business Strategic Orientation, Information Systems Strategic Orientation, and Strategic Alignment. Information Systems Research, 8(2):125–150.
49. Chari, S., Balabanis, G., Robson, M. J., & Slater, S. (2017). Alignments and Misalignments of Realized Marketing Strategies with Administrative Systems: Performance implications. Industrial Marketing Management, 63, 129–144.
50.  Chia-Huei, w., Zichun, Y., Sang-Bing, T., Weirian, W., Boshu, C., &Xin, L. (2020). An Empirical Study on Sales Performance, Effect and Pricing Strategy for E- Commerce: From the Perspective of Mobile Information. MobileInformationSystem 2020.
51.  Chin, W., W. Marcolin, B. L., &Newsted, P. R. (1998). A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2):189–217.
52. Chinyere, C.O., Nwanneka C.G., &Okwor, H. (2018). Does Promotional Strategy Affect Sales Growth of Manufacturing Firms in South-East Nigeria? European Journal of Management and Marketing Studies.
53. Clark, K. B., & Fujimoto, T. (1991). Product Development Performance: Strategy, Organization, and Management in the World Auto Industry. Boston: Harvard Business School Press.
54. Colin, T.S. and Jan D, H. (2014). The Impact of Persuasive Messages on IAT Performance. Journal of Consumer Behaviour, Vol.15 (6) 13-16.
55. Conner, K., &Prahalad, C. (1996). A Resource-Based Theory of the Firm: Knowledge versus opportunism. Organization Science, Vol.7, No.5, pg.477-501.
56. Cooper, R., &Kleinschmidt, E. (2015). Benchmarking the Firm’s Critical Success Factors in New Product Development. The Journal of Product Innovation Management, 12 (5):74-391.
57. Cross, O. D. (2018). Effect of Marketing Strategies on Organizational Performance. International Journal of Business, Marketing, and Management, 3(9):01-09.
58. Daft, L., R. (2000). Organization Theory and Design (9thed.) South – Western College Publishing, Thomson Learning U.S. A.
59. Daniel, R.V. (2003) Persuasive Behaviour as a Moderator of the Relationship between Strategic Marketing and Performance. California State University, Sen Bernardino.
60. Daniel,F.,&Juan,S. (2010). When Necessity Becomes a Virtue: The Effect of Predict-Market Competition on Corporate Social Responsibility. Journal of Economics & Management Strategy, 19(2): 453-487
61.  David, J. (2007). Principles and Practice of Marketing, 5th Edition.McGraw-Hill Education.Berksshire.
62. Deonir, D.T., Gabriel, S. M. Evandro, B. S., and Fabiano, L. (2017). Pricing Strategies and their Impact on Corporate Profitability. RevistadeAdministracas (Sao Paulo), 52 (2): 102-133.
63. Destya, A. S., Trisandi, E.P., & Sri, M. (2020). The Effects of Product Diversification Strategy, Financial Leverage, Firm Size and Capital Structure on Profitability Journal of Accounting for Sustainable Society (JASS), 02(02):1-13.
64.  Donald, I. O., & Etokakpan, M. U. (2013) the Impact of Sales Promotion on Sales Volume: A Case of Selected Telecommunication Firms in Nigeria. Journal of Marketing Management, 12(2):23
65. Doving, E., & Gooderham, P. (2008). Dynamic Capabilities as Antecedents of the Scope of Related Diversification: the case of small firms’ accountancy practices. Strategic Management Journal, 29(8):841-857. http://dx.doi. org/10.1002/smj.683.
66. Drazin, R., & Van de Ven, A. H. (1985). Alternative Forms of fit in Contingency Theory. Administrative Science Quarterly, 30(4):514–539.
67. Dudu, O. F (2018). A Review of the Effect of Pricing Strategies on the Purchase of Consumer Goods. International Journal of Research in Management, Science & Technology, 2(2):2018.
68. Ebitu, E. T. (2016). Marketing Strategies and the Performance of Small and Medium Enterprises in AkwaIbom State, Nigeria. British Journal of Marketing Studies, 4(5):51-62.
69. Ebitu, E. T., Basil, G., & Ufot, J. A. (2015). An Appraisal of Nigeria’s Micro, Small and Medium Enterprises (MSMEs): Growth, Challenges and Prospects. International Business and Education Conference, 8:7-11.
70.  Emma, M. N. (2016) the Effect of Product Innovation on Profitability of Private Manufacturing Firms in Nairobi Country. University of Nairobi.
71. Eniola, A. A., &Olorunleke, G. K. (2020). Marketing Strategies Impact on Organizational Performance. International Journal of Science and Technology Research, 9(1):2-6.
72.  Eon Van der, M. S, & Jhandre, B. (2013). Originator and Generic Medicine: Pricing and Market Share. International Journal of Pharmaceutical and Healthcare Marketing, 7 (2): 104-119.
73. Farris, Paul, W., Neil, T., Bendle, P. E., Pfeifer, & David J., R. (2010).Marketing Metrics: The Definitive Guide to Measuring Marketing Performance. Upper Saddle River, New Jersey: Pearson Education, Inc. ISBN 0137058292.
74. Fink, L., & Neumann, S. (2009). Exploring the Perceived Business Value of the flexibility enabled by Information Technology Infrastructure. Information & Management, 46(2):90–99.
75. Food and Agriculture Organization (FAO), (2007). Agroindustry for Development, VialeTerme di Caracalla, Italy.
76. Food and Agriculture Organization (FAO)/United Nations Industrial Development Organization (UNIDO) and International Fund for Agriculture Development (IFAD), (2008). Report of the global agro-industries forum, New Delhi, India, Improving competitiveness and development impact, Rome: FAO 2008, 53 pages
77. Food and Agriculture Organization, (1997). The Agro-processing Industry and Economic Development. http://www.fao.org/decrep/w5800e/w5800e12.htm/ accessed on February 19, 2020.
78. Francis, J. M., & Robert, P. L (1991). Implicit Price Bundling of Retail Products: A Multi-Product Approach to Maximizing Store Profitability. Journal of Marketing, 55(4):63-73.
79. Francis, O.A., & Mathenge, P.M. (2015). Effect of Promotional Element on Sales Volume of Financial Institutions in Kenya: Case Study of Kenya Post Office Savings Bank. Journal of Marketing and Consumer Research, 11(11):2422-8451
80.  Fred, L., & Erik. J. H. (2006). The Impact of Product Innovativeness on the Link Between Development Speed and New Product Profitability. Journal of Product Innovation Managements 23 (3): 203-214
81. Gabriela, S., & Ricardo, P. D. Q. (2017). The Impact of Diversification in the Operations Strategy of Capital Goods Companies. Programa de Pós-graduaçãoemEngenhariaMecânica, Centro Universitário da FEI, Av. Humberto de Alencar Castelo Branco, 3972, Bairro Assunção, CEP 09850-901, São Bernardo do Campo, SP, Brazi.
82. Gbolagade, A., Adesola, M. A., & Oyewale, I. O. (2013). Impact of Marketing Strategy on Business Performance: A study of Selected Small and Medium Enterprise (SMEs) in Olyola Local Government, Ibadan, Nigeria. Journal of Business and management, 11(4):59-66.
83. Geoffery, (2021). Effect of marketing Strategies on Product line Margin among the Cassava Microenterprises in Nigoni County, Kenya. Thesis submitted to post graduate school, Egerton University, Kenya.
84. Gerald, E. F. (1990). Multiple out Come from Product R&D: Profit Ability Under Different Strategic Orientations. Journal of Management Science, 16(3):633-636.
85. Geroski, P. M., Machin, S., &Reenen, J. (1993). The Profitability of Innovating Firms. Rand Journal of Economics, 24(2):198–211.
86. Goedhuys, M., and Veugelers, R. (2008). Innovation Strategies, Process and Product Innovations and Growth: Firm-level evidence from Brazil. Department of Managerial Economics, Strategy and Innovation (MSI)
87. Goi, C.L. (2009). A Review of Marketing mix: 4Ps or more? International Journal of Marketing studies, 1(1):2.
88. Graham, J., R., Michael, L., & Jack, W. (2002). Does Corporate Diversification Destroy Value? Journal of finance, 57, 695-720.
89. Grawe, K. (2017). Neuropsychotherapy: How the Neurosciences Inform Effective Psychotherapy (1^sted.). Routledge.
90. Grönroos, C. (1994). Facing the Challenge of Service Competition: The Economies of Service. In Kunst, P. and Lemmink, J. (Eds). Quality Management in Services. Van Gorcum, Assen, Maastricht. The Netherlands, pp. 29-40.
91. Grönroos, C. (1999). From Marketing Mix to Relationship Marketing: Towards a ParadigmShift in Marketing Management Decision, 32(2): 4-20.
92. Gupa, S. L., & Arum, M. (2008). Comparative Study of Promotional Strategies Adopted by Public and Private Sector Banks in India. Journal Indexing and Metrics, 4(3):1177.
93. Gupta, S. (2007). The Impact of Sales Promotion on when, what, and how much to Buy. Journal of Marketing Research, (25):342-355
94. Haghighinasab, M., Sattari, B., Ebrahimi, M., &Roghanian, P. (2013). Identification of Innovative Marketing Strategies to Increase the Performance of SMEs in Iran. International Journal of Fundamental Psychology & Social Sciences, 3(2):26-30.
95. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2019). Partial least squares structural equation modeling (PLS-SEM): A guide for practicing researchers (3rd ed.). Sage.
96. Henderson, J. C., &Venkatraman, N. (1989). Strategic Alignment: A framework for Strategic Information Technology Management. Cambridge, MA: Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology.
97. Higgins, J. M., and Vincze, J. W. (1993). Strategic Management: Concepts and Cases. Chicago IL Dryden Press.
98. Hooper, V. A., Huff, S. L., &Thirkell, P. C. (2010). The Impact of IS-Marketing Alignment on Marketing Performance and Business Performance. SIGMIS Database, 41(1):36–55.
99. Howard, G. (2011) changing Minds. The Art and science of changing our own and other people’s Mind.
100. Hui, Y., Ding, Z., & Chen, Z. (2017). The Influence of Reference Management Settings. International Transactions in Operational Research, 24 (4): 907-924
101. Hulland, J., & Hulland, M. (2015). The role of structural equation modeling (SEM)in marketing research. Journal of Marketing Research, 52(4), 538-554.
102. Ibojo, B. O. (2015). Impact of Strategic Marketing on Organizational Performance of Food and Beverage industry. Department of Business Administration, AjayCrowther University, Oyo State, Nigeria.
103. Ibrahim, A. A. (1997). The Socioeconomic Implication of Zaki Flour Mills on inhabitant of Azare. Geography Department F.U.T Yola.
104. Iheanacho, A. C., &Iheanacho, A., A. (2012). Research Methodology for Social Sciences and Education, Stirling-Horden Publishers Ltd., Ibadan.
105. Iheanacho, A. C., &Iheanacho, A., A. (2012). Research Methodology for Social Sciences and Education, Stirling-Horden Publishers Ltd., Ibadan.
106. Iivari, J. (1992). The Organizational fit of Information Systems. Information Systems Journal, 2(1):3–29.
107. Inam, A. H. (2022). The Effect of Pricing Strategy onCompetitive Advantage. American Journal of Economics and business management, 5 (II):250-264.
108. Industry: A perspective of resource-based view theory. Journal of Project Management, vol. 6. Pp. 45-56.
109. Isibor, O. F., &Gbandi, E. C. (2020). Strategizing for Growth with a Niche: Market Penetration as a Viable Alternative in the Hospitality Sector. Uniport Journal of Management, 5(1):1-12.
110. Jain, S. C. (2000). Marketing Planning & Strategy (6th Ed.). Cincinnati, OH: South-Western College Publishing.
111. Jasmine,Y.K. (2016). Examining the Relationship between Organizational Constraints and Individual Deficits in Executive Functioning on Employees’ Extra-Role Work Behaviors. A Dissertation Presented to the Graduate College of Burling Green State University in Partial Fulfillment of the Requirement for the Degree of Doctor of Philosophy.
112. Jaworski, B. J., &Kohli, A. K. (1993). Market Orientation: Antecedents and Consequences. Journal of Marketing, 57(3):53.
113. Jay, S. (2008).Situational Constraints and Personality as Antecedents of Organizational Citizenship Behavior. A Dissertation Presented to the Graduate School of Clemsom University.
114. John, G.W., Sela, S., and shreyas, G. (2018). The Mediating role of Persuasiveness on Mood and Personal Relevance effects ofGain-Framed and loss-Framed Health Messages. Journal of Marketing Communication, vol.22 (17): 180-196.
115. Kannan, P., &Saravanan, R. (2012).  Diversification – Strategies for Managing a Business. International Journal of Multidisciplinary Management Studies, 2(5).
116. Kearns, G. S., &Sabherwal, R. (2006). Strategic Alignment between Business and Information Technology: A knowledge-based view of Behaviors, Outcome, and Consequences. Journal of Management Information Systems, 23(3):129–162.
117. Keith, C., & Anthony. J. R. (1970). Price, Quality and Market Share. Journal of Political Economy, 78 (6):1292 – 1309.
118. Kelly, W., Hongman, G. Bahram, A., &Dharam, S. R. (2010). An Empirical Study of Impacts of Production Mix, Product Route Efficiencies on Operations Performance and Profitability: A Reverse Logistics ‘Approach. International Journal of Production Research, 48(4):1087-1104
119.  Kidus,M.(2019). The Impact of Sales Promotion on the Sales Volume in Business.
120. Kimathi, D. K. (2022). The Effect of Pricing Strategies on the Performance of Micro, Small and Medicine Enterprises (MSMEs) in Kenya. Journal of Entrepreneurship &ProjectManagements, 5, (1):2012.
121. Kimbell, R.(2002). An Exploratory Report of Sales Promotion Management. Journal of Consumer Marketing, 9 (3):65-75
122. King, W. R., &Pollalis, Y. A. (2000). IT-based Coordination and Organizational Performance: A gestalt approach. The Journal of Computer Information Systems, 41(2):64.
123. Kisaka, L. M. (2012). The Relationship Between Marketing Strategies and the Performance of Savings and Credit Societies in Mombasa District, Ph.D Dissertation, School of Business, University of Nairobi, Kenya.
124. Kor, Y., &Leblebici, H. (2005). How do Interdependencies among Human Capital Deployment, Development, and Diversification Strategies Affect Firms’ Financial Performance? Strategic Management Journal, 26(10):967-985. http://dx.doi.org/10.1002/smj.485
125. Kotler, P. (2000). Marketing Management Millennium Edition, Tenth Edition, Prentice-Hall, Inc.
126. Kotler, P., & Keller, K. (2006), Marketing Management, 12nd Edition. India; Peearson Pearson Education Incpg 8-25.
127. Kotler, P., &Armstrong, G. (2006), Principles of Marketing, 11th Edition. New Jersey: Peeason Pearson Education Plc Ltd.
128. Kotler, P: & Armstrong, G. (2013). Principles of Marketing. 14^th Edition. Edinburgh Gate Harlow Essex, England. Pearson Education.
129. Kumar, V., & Petersen, J. A. (2005). Using a Customer-Level Marketing Strategy to Enhance Firm Performance: A Review of Theoretical and Empirical Evidence. Journal of the Academy of Marketing Science, Volume 33, No. 4.
130. Lamont, & Geiger, (2004).Diversity, Diversification and Profitability among British Manufacturing Companies. Academic of Management Journal, 31, 771-801.
131. Larry, P.P. (2002). The Profit Effects of Product-Market Growth Strategy: A Financial Service Example. Journal of financial Services Marketing, 7(3):258-266
132. Lin, L. H. (1993), “Consumer Product Classification, Innovation Type and New Product Marketing Strategy.” National Cheng Chi University Department of Business Administration of PhD Thesis.
133.  Liu, G.J., Shah, R., & Schroeder, R.G. (2011). Limcing Work Design to Mass Customization: A Socio-Technical Systems Perspective. Journal of Retailing 37(4):519-545.
134. LiuruiD.,&Shenggang, Y. (2016). Pricing Strategies Uncertain Market Size in the SecondPeriod.Journal of Applied Mathematics.
135. Loes, J., Marieke, L., F., wulff, R. and Reijmersdal, E.,A., v. (2016). Brand Placement Disclosure effect on Consumer Self-control: the Mediating role of Persuasion. Journal of Consumer Behaviour, Vol. 15(6): 34-38
136. Love Lock, C. (2001) Curing and Caring in Surgical Services: Relationship Approach. Journal of Service Marketing
137. Luxember, & Tan (2004).Corporate Distinctive Competence, Strategy, Industry and Performance. Strategy Management Journal, 6(3):273-293.
138. Mahabubur, R., Mary, L., & Raid, S. (2020). Cross-border Mergers and Acquisitions: Impact on Marketing Capability and Firm Performance. Journal of General Management, Pp. 1-15.
139. Markides, C.C. (2004).  Consequences of Corporate Refocusing: Extant evidence. Academy of Management.  35, 397-412.
140. Mason, C., Botelho, T., and Zygmunt, J. (2017). Why Business Angels Reject Investment Opportunities: is it Personal? International Small Business Journal: Researching Entrepreneurship, Vol.  35(5): 519 – 534. doi: 10.1177/026624616622
141. Masood, H., Saif, U. Q., Sidra, S., &Aamna, K. (2013). Impact of Marketing Strategy Creativity on Organizational Performance via Marketing Strategy Implementation Effectiveness: Empirical Evidence from Paskistani Organizations. Meddle-East Journal of Scientific Research, 16(2):264-273.
142. Masy, W. G. (2018). Organizational Constraints and Performance of Solid Waste Management Projects in Nairobi City County, Kenya. A Dissertation Presented to the Graduate School, of Kenyatta University Kenya.
143. McCarthy, E., J. (1960).  Basic Marketing: A Managerial Approach, Homewood, IL: Richard D. Irwin.
144. McDaniel, S. W., &Kolari, J. W. (1987). Marketing Strategy Implications of the Miles and Snow Strategic Typology. Journal of Marketing, 51(4), 19–30.
145. Mehta, G.S. (2012). Agro-processing in Uttar Pradesh: Emerging structure and development potentials. Giri Institute of Development Studies. (December 15, 2012)
146. Michael, C., &Parsa, H. G. (2006). Pricing Strategies on Maximize Revenue in Lodging Industry. InternationalJournal of Hospitality Management, 25, (I):91-107.
147. Min, S., Song, S., & Keebler, J. S. (2002). An Internet-Mediated Market Orientation (IMO): Building a Theory. Journal of Marketing Theory & Practice, 10(2):1–11.
148. Montgomery, C.A. (1994).  Corporate Diversification. Journal of Economic Perspectives, 8(3), 163-178.
149. Mota, J., & Castro, L. (2004). A Capabilities Perspective on the Evolution of Firm Boundaries: a comparative case example from Portuguese molds industry. Journal of Management Studies, 41(2):295-316. http://dx.doi. org/10.1111/j.1467-6486.2004.00433.
150. Muriithi, R. M., &Waithaka, P. (2020). Market Penetration Strategy and Performance of Agrochemical Companies in Nakuru County, Kenya. Journal of Strategic Management, 4(4):23-33.
151. Musa, G. K. K. (2018). Product Development and Management Strategies. Department of Marketing, School of Management Studies, Kano State Polytechnic, Kano, Nigeria.
152. Nadler, D. A., &Tushman, M. L. (1980). A Model for Diagnosing Organizational Behavior. Organizational Dynamics, 9(2):35–51.
153. Nalk, A.L. (2011). Corporate Economic Performance: Diversification Strategy versus Market Structure. Strategic Management Journal, 2(4):327-343.
154. Nguyen, H. T., Rewel, J. S. J., Nguyen, O. M., Le Doan, M. D., & Nguyen, V. T. L. (2020). Analysis of Mega Market’s Market Penetration Strategy in VietnamFMCGIndustry. International Journal of Educational Research and Development, 2(2):05-08.
155. Ocholi, A., Inyommom, A., E. and Iheanacho, A., c. (2018). Effect of Working Capital Management on the Performance of Quoted Agro-allied firms in Nigeria. GRA’S Multidisciplinary international (GRAMI) journal, Volume 2 (6), 2018.
156. Oh,W., and Pinsonneault, A.(2007).On the Assessment of the Strategic value of Information Technologies: Conceptual and Analytical Approaches. MIS Quarterly, 31(2):239–265.
157. Olomola, A. S. (2001). Strategies and Impact of Agro-allied parastatals Reform in Nigeria. Agricultural Economics, 24 (2):1-4.
158. Olson, E. M., Slater, S. F., &Hult, G. M. T. (2018). The Application of Human Resource Management Policies within the Marketing Organization: The Impact on Business and Marketing Strategy Implementation. Industrial Marketing Management, 19(69):62-73.
159. Onodugo, V.A., Ugwuonah, G.E., &Ebinne, E.S. (2010). Social Science Research: Principles, Methods and Applications. EL DEMAK Publishers, Enugu, Enugu State, Nigeria.
160. Onwumere, J. (2008). Policy Issues in enhancing the output of Agricultural Business Small and Medium Scale Piggery Enterprises (AGRI-SMEs) in Abia State. Journal of Agricultural Extension, 12(2):4-8.
161. Osuagwu, L. (2006). Market Orientations in Nigerian Companies, Intelligence and Planning, 24, (6), 67-73.
162. Owomoyela S.K., Oyeniyi, K.O., & Ola, O. S. (2013).Investigating the Impact of Marketing Mix elements on Consumer Loyalty: An Empirical Study on Nigerian Breweries Plc. Interdisciplinary Journal of Contemporary Research in Business. 4(11):485 –496.
163. Oyefesobi, O. O., Akintunde, S. O., &Aminu, S. A. (2017). Diversification Strategy and Organization Market Share in the Nigeria Manufacturing Industry. Ilorin Journal of Management Science, 4(1):19-31.
164. Paul, H., Ian, R.H., Robert, E.M., Matthew, H.,&Chih-Hsien, L.(2020). Product-Market Planning Capability and Profitability: Journal of Industrial Marketing Management, 90(9):370-379.
165. Penrose, E. D. (1959). The Theory of the Growth of the Firm. New York: Oxford University Press.
166. Peter, W.R. (1999) Product Innovation, Product-Market Competition and Persistent Profitability in the US Pharmaceutical Industry. Strategic Management Journal, 20(7):655-670.
167. Peyman, A., &Hamid-Reza, J. (2015). Analysis of Marketing Strategy Impact on Organizational Performance in Iran Banking Industry. Shiraz Journal of System Management, 3(1):41-50.
168. Porter, M.E. (1985). Competitive Advantage. New York: Free Press, pp. 42-43.
169. Praveess, A.,&Tanvi, C. (2015). Effect of Store Location, Product Variety and Quality on Customer Loyalty towards Fruits and Vegetable Services. Prestige Institute of Management, Gwalior.
170. Pullman, G.(2013). Persuasion. History, Theory, Practice. Indianapolis. IN: Hackett Publishing Company.
171. Putu, G. S., Ni Nyoman, K. Y., Henny, R., Made, S., & Ida, B. A. (2020). Competitive Advantage to Enhance Internationalization and Marketing Performance Woodcraft.
172.  Rainer, O., Hans,C. J., &Michael,H.(2017). Effects of Pricing Strategies and Product Quality on Private Label and National Brand Performance. Journal of Retailing and Consumer Service, 34(4):294-301.
173.  Ramunuj, M. (2006). The Relationship of Intellectual Capital, Innovation and Organizational Performance: A Preliminary Study in Malaysia SMEs. International Journal of Management Innovation Systems, 1(1):1-13
174.  Rapert, M.I., Linch,D., &Suter, T. (2008).Performance via Strategic Consensus and Commitment. Journal of Strategic Marketing. 4(4):193-205.
175. Rathnam, R. G., Johnsen, J., & Wen, H. J. (2004). Alignment of Business Strategy and IT strategy: A case study of a fortune 50 financial services company. Journal of Computer Information Systems, 45(2):1–8.
176. Robert, P.A. (1983). Whatever Become of consistency theory? Personality and social psychology Bulletin, Vol. 9(1): 37 – 54.
177. Rogers, P. R., Miller, A., & Judge, W. Q. (1999). Using Information-processing Theory to Understand Planning/Performance Relationships in the Context of Strategy. Strategic Management Journal, 20(6):567.
178. Rumelt, R. (1974). Strategy, Structure and Economic Performance. Cambridge: Harvard University Press.
179. Sande, N. A., & Tabitha, W. (2020). Influence of Market Penetration Strategy on Organizational Performance of Public Universities in Kenya. International Research Journal of Business and Strategic manager, 1(1):48-58.
180. Sara, A., & Uma, S.S. (2019). The Effect of Pricing Strategy on Customer Retention in Kurdistan. International Journal of Supply Chaim Management, 8(1):98-112.
181. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool for business research. European Journal of Marketing, 48(10/11), 1250-1278.
182. Schumpeter, J. (1934). The Theory of Economic Development. Cambridge, MA: Harvard University Press.
183. Shaul, O, Noga, s. (2014). Source Personality and Persuasiveness; Big five Predispositions to being Persuasive and the role of Message Involvement. Journal of personality, 82:3 June 2014
184. Shomi, P., David, J. A., Alexandra, K.,& Paul, E.S. (2018). Organizational Constraints and Performance: an Indirect Effect Model. University of South Florida, Yampa, Florida USA.
185. Shore, B. (1996). Using Information Technology to Achieve a Competitive Advantage: A study of current and future trends. Journal of Computer Information Systems, 36(4):54–59.
186. Shuba,S., & Frank, M.B. (200). Market Share Response and Competitive Interaction. The Impact of Temporary, Evolving and Structural Changes in Prices.Journal of strategic Marketing, 17 (4).
187. Singh, D. A., Gaur, A., &Schmid, F. (2010). Corporate Diversification, TMT Experience and Performance. Management International Review, 50(1):35-56. http:// dx.doi.org/10.1007/s11575-009-0025-4.
188. Slater, S. F., &Narver, J. C. (1994), “Does Competitive Environment Moderate the Market Orientation Performance Relationship?” Journal of Marketing, 46, 46-55.
189. Soojung, k., Joonghwa, L., Yoori, H., and Hoo, J. (2016). Effects of Prominent In-Game Advertising in Mobile Media: Cognitive, Effective, and Behavioural Outcomes and the Moderating role of Persuasion Knowledge. International Journal of Mobile Communications, vol.14(3):203-225
190. Spillan, J., & Parnell, J. (2006). Marketing Resources and Firm Performance among SMEs. European Management Journal, 24(2-3):12-23. doi:10.1016/j.emj.2006.03.013.
191. Staniewski, M., and Awruk, k. (2016) Start-up Intentions of Potential Entrepreneurs-the Contribution of hope to success. Economic Research-Ekonom-Ika proceedings, 1,6,doi: 10. 5465/ambpp.2016.160
192. Stavros, K., Katerina, M., &Olyumpia, P. (2020). Global Marketing Strategic Approaches on Multi-national Companies Product Development. International Journal of Scientific Research and Management, 8(12).
193. Strahle, W.M., Spiro, R. L., &Acito, F. (1996). Marketing and Sales: Strategic Alignment and Functional Implementation. Journal of Personal Selling & Sales Management, 16(1):1–20.
194. Sudek,R. (2007). Agel Investment Criteria, Journal of small business strategy, 17(2),89-103
195.  Sureerat, M., Prathanporn, J., &Sutana, B. (2015). Proactive Product Strategy and Marketing Profitability: An Empirical Investigation of Furniture and Decorative Product Businesses in Thailand. MahaSarakham Business School, Mashasarakham University.
196. Tai-Wei, C., & Cheng-Ze, H. (2021). How to Shape the Employees’ Organizational Sustainable Green Knowledge Sharing: Cross-level Effect of Green Organizational Identity Effect on Green Management Behavior and Performance of Members. Graduate School of Resources Management and Decision Science College, National Defense University, Taipei City 112, Taiwan.
197. Teece, D. (1982). Towards an Economic Theory of the Multi-Product Firm. Journal of Economic Behavior & Organization, 1(1):39-62.
198. Thompson, A.A., Strickland, A.J., & Gamble, G.E. (2010).Crafting and Executing Strategy: Text and reading (17th Ed.).Ohio: Thompson Western.
199. Till, B. D., & Nowak, L. I. (2000). Toward Effective Use of Cause-Related Marketing Alliances. Journal of Product & Brand Management, 9(7):472-484.
200. Tobia, k., c., T., Andreas, s., and Alexander, K. (2019). The role of Persuasion in Corporate Idea Contests. Journal of Product Innovation Management vol.36 (5): 560-585
201.  Tougher, S., Yazoume, Y. John, H.A., Idrissa, A. K. & Thomson, R. (2012). Effect of the AffordableMedicines Facility- Malaria (AMFM) On the Availability, Price and Market Share of Quality Assured Artemisinn-Based Combinationtherapies. The lancet, 380(9857): 1916- 1926.
202. Uchenna, I., Ikenna, O., Emmanuel, A.J. & Emmanuel, E.(2010). Effect of Promotional Strategies of Pharmaceutical Companies on Doctor’s Prescription Pattern in South-East Nigeria. TAF Preventive Medicine Bulletin, 9(1):2010.
203. Valos, M. J., &Bednall, D. H. B. (2010). The Alignment of Market Research with Business Strategy and CRM. Journal of Strategic Marketing, 18(3):187–199.
204. Vincent, N.O.A., Ireneus, C.N., Promise, C.O. &Ifeanyichukwu, C.D.(2018). Sales Promotion as a Leverage Strategy for Improving Sales Volume and Profit Ability in Alcohol Beverage Industry. International Research Journal of Management, IT and Social Sciences, 5(4):18-25.
205. Vorhies, D. W., & Morgan, N. A. (2003). A Configuration Theory Assessment of Marketing Organization fit with Business Strategy and its Relationship with Marketing Performance. Journal of Marketing, 67(1):100–115.
206. Voss & Voss (2000). Strategic Orientation and Firm Performance in an Artistic Environment. Journal of Marketing.
207. Wairimu, E.K. (2011). The Effect of Marketing Promotional Strategies on Sales Volume: A Case Study carried out on Multi-choice Kenya Limited. A Dissertation Presented to College or Business Management, Kampala International University.
208. Walker, O. C. Jr., &Ruekert, R. W. (1987). Marketing’s Role in the Implementation of Business Strategies: A critical review and conceptual framework. Journal of Marketing, 51(3):15–33.
209. Wangui, W. L., &Kiragu, D. (2018). Effect of Pricing Strategy and Growth of Selected Hotels in Nyeri country, Kenya.
210. William, B., & Markus, c. (2009). Pioneering plus a Broad Product Line Strategy: Higher Profits or Deeper Losses?Journal of Management Science, 55(6):958-967.
211. Woo, G., k., Jun, J., L., and Robert, A., B. (2016). The Impact of Social Media Reviews on Restaurant Performance! The Moderating role of Persuasion.International Journal of Hospitality Management, vol. 55(10):41-51
212. World Bank, (2007). World Development Report: Agriculture for Development, Washington, DC.
213. World Bank, (2018). Conflict and Violence in Nigeria: result from the North East, North Central, and South-South Zones.
214. Wu, I. L. (2013). The Antecedents of Customer Satisfaction and its Link to Complaint Intentions in Online Shopping: An Integration of Justice, Technology, and Trust. International Journal of Information Management, 33(1):166–176.
215. Yildiz, S. M. (2018). An Empirical Analysis of the leader-member Exchange and Employee turn-over Intentions Mediated by Mobbing: Evidence from Sport Organizations. Economic Research-EK OnomskaIstrazivanja, 31(1), 480-497. Doi:101080/1331677x.
216. Ying, K., and Lingling, Z. (2014). When does Green Advertising work? The Mediating role of Persuasion. Journal of Marketing Communications, vol.20 (3):197-213
217. Zaefarian, G., Henneberg, S.C., &Naudé, P. (2013). Assessing the Strategic fit Between Business Strategies and Business Relationships in Knowledge-Intensive Business Services. Industrial Marketing Management, 42(2), 260–272.
218. Zeithaml, V. A., Varadarajan, P. R., &Zeithaml, C. P. (1988). The Contingency Approach: Its Foundations and Relevance to Theory Building and Research in Marketing. European Journal of Marketing, 22(7):37–64.
219. Zmud, R. W. (1984). An Examination of “Push-Pull” Theory Applied to Process Innovation in Knowledge Work. Management Science, 30(6):727-738. doi: 10.1287/ mnsc.30.6.727.
220. Zott, C., and Amit, R. (2007). The Fit between Product Market Strategy and Business Model: implications for firm performance. Strategic management journal.