The Role of Entrepreneurial Marketing in the Indonesian Agro-Based Industry Cluster to Face the ASEAN Economic Community

Abstract

The agro-industry plays an important role in the Indonesian economy. However, Micro, Small and Medium Enterprises (MSMEs) in this industry face various challenges to be competitive, one of which is the implementation of the ASEAN Economic Community (AEC) agreement. The study aims to develop a model for the agro-industry Micro-Small Medium Enterprises to face the ASEAN Economic Community (AEC) using entrepreneurial marketing approaches. This research was conducted at three centers of agro-based industry clusters in Indonesia. The respondents were business actors representing the leading Micro-Small and Medium Enterprises (MSMEs) in the fruit-processing industry cluster, fish processing industry cluster and milk processing industry cluster. Primary data collection was conducted through structured interviews using close ended questionnaires. This study used Structural Equation Modelling analysis with a Partial Least Squares (PLS) approach to develop a model of enhancement for the agro-industry clusters in dealing with AEC. The findings suggest that the entrepreneurial marketing has significant impact in increasing business development and business competitiveness. The MSMEs possessing great competitiveness would be more ready to penetrate their business into other ASEAN markets. In addition, supportive government policies that encourage and improve innovativeness can significantly increase the entrepreneurial marketing ability.

1. Introduction

Agro-industry is an activity that utilizes agricultural products as raw materials. Processes used include processing and preservation, storage, packaging and distribution. The agro-industry is understandably one of Indonesia’s top industries due to its strategic importance to the national economy. In the future, the agro-industry will be supported by renewable resources generated by agricultural, fishery, forest, animal husbandry, and plantation/forestry sectors. Indonesia’s agro-industries are significant economic clusters contributing to both the country’s GDP (Gross Domestic Product) and employment prospects. Based on the 2021 Ministry of Industry data, the agro-industry contributed 50.59% to the GDP growth of the non-oil and gas processing industry [1,2]. The number of workers in the agro-industry particularly agricultural sector contributed 29.76% to the total national workforce [3].

In an effort to develop the agro-industry, the Indonesian government has paid particular attention to several strategic issues, such as the food and beverage industry’s readiness to face the 2025 ASEAN Economic Community (AEC), the revitalization of the sugar industry, and the legality of raw materials used in the processed-wood-industry [1]. Based on discussions in the Prepared Foodstuff Product-Working Group (PFPWG) of the ASEAN Consultative Committee on Standards and Quality (ACCSQ), it is understood that the food sector has been targeted for acceleration. The AEC is an agreement among ASEAN nations to establish a single trade area that will improve economic competitiveness in a regional market of approximately 500 million people [4].

The AEC has opened up both significant opportunities and substantial challenges for the Indonesian economy. Until now, Indonesian trade has not been able to exploit the significant market potential of ASEAN. In the period of January–August 2013, Indonesian exports to the ASEAN market reached a comparatively low 23% of total export value [4]. In addition, the level of utilization of ASEAN tariff preferences used by Indonesian exporters to penetrate the ASEAN market reached only 34.4% [4]. Indonesia’s trade surpluses have not yet reflected the strength of its domestic exports. This is because Indonesia’s processing industry is still dependent on imported raw materials, leaving the industry extremely vulnerable to the availability of raw materials from the global markets. In addition, Indonesia’s global competitiveness index ranking is still 38th out of 148 countries [4]. Neighboring countries such as Singapore occupy second position, while Malaysia is 24th. Thus, it is feared that Indonesia will become one of many producers of similar products competing within ASEAN countries.

Indonesia is known to have a good reputation as a reliable producer of agricultural, marine and plantation products. However, the relative strength of these products is not yet sufficient to balance the import of finished goods. Micro, Small and Medium Enterprises (MSMEs) are one of the driving forces in economic development [5]. In ASEAN nations, MSMEs are no exception, being the backbone of the national economies and the integral component for achieving sustainable long-term economic growth. Although the role of MSMEs is vital, ironically, the level of productivity and competitiveness of MSMEs in Indonesia is still relatively low. According to [6], this is related to: (i) the lack of an adequate level of personnel training available to run micro businesses, and (ii) the lack of entrepreneurship competency within micro businesses. In addition, Indonesian MSMEs also face the classical issues of limited access to both capital and information technology, and the impact of globalization leading to various international free-trade agreements. It can be seen therefore that strengthening the competitiveness of MSMEs through appropriate marketing strategies will be a key factor in facing the challenges and appropriately fulfilling the opportunities that the AEC provides.

Marketing concepts of large businesses are different from those of MSMEs. Entrepreneurial marketing (marketing entrepreneurship) is a relatively new approach to marketing concepts which is more appropriate to the address resource constraints and other problems that exist in MSMEs [7,8]. The various specific problems of MSMEs make these businesses less able to apply the concept of a marketing mix which can be utilized by big businesses. The Entrepreneurial Marketing (EM) approach used by MSMEs is designed more towards a concept of marketing which emphasizes intuitive estimation, innovative orientation and interactive marketing methods. The ability of entrepreneurial marketing by MSMEs directly affects their development and business sustainability [9].

The goal of this research is to develop the agro-based industries in order for them to recognize their internal problems, as well as the general marketing challenges, such as implementations within the AEC. The specific objective of this research is to develop a model for the agro-industry MSMEs, based on the need for agricultural development to face the AEC using entrepreneurial marketing approaches.

2. Literature Review

2.1. Entrepreneurial Marketing

Entrepreneurial marketing has been defined as a proactive attitude in identifying and exploiting opportunities in order to gain and retain profitable customers through innovative approaches to managing risk, optimizing resources, and creating value [7,10]. A number of entrepreneurial characteristics appear to contradict with traditional marketing. The entrepreneurial marketing concept is focused on innovations and intuitive approach to understand the market needs, but in traditional marketing assumes that the customer needs proceed through formal assessment.

In addition, successful MSMEs generally practice a bottom-up strategy. The stages begin by identifying market opportunities, testing them with trial and error methods then serving the needs of consumers interactively. Once this is completed, new customers will appear with homogenous profiles of their businesses. Thus, in the concept of MSME entrepreneurial marketing, the target market is formed by the process of natural elimination and selection [11]. Although there are differences between traditional marketing and entrepreneurial marketing approaches, the main point of difference between these two concepts is not in the marketing mix but in the difference in content and combinations in it.

2.2. Agro-Based Industry Cluster

A significant analysis of agribusiness was presented in the review by [12] with companies that processed plant-based (plant-derived) or animal-based (produced by animals) products. The process includes alteration and preservation through physical or chemical treatment, storage, packaging and distribution. The agro-industry is engaged in the utilization of agricultural products as raw materials. In the agro-industry business based, farmers should improve the technology of pre- and post-harvest handling to obtain the competitiveness among traders, entrepreneurs and consumers [13]. The sector has strategic value because of its position as a bridge between the agricultural sector on both upstream and downstream activities. Given its strategic position, the agro-based industry is a priority industry cluster in the Indonesian National Industrial Policy Road Map. The development of industrial clusters is based on 12 industries, namely: (1) Palm Oil Processing, (2) Rubber and Rubber Goods, (3) Cocoa Processing, (4) Coconut Processing, (5) Coffee Processing, (6) Fruit-processing, (7) Tobacco Production, (8) Fruit-processing, (9) Furniture Making, (10) Fish Processing, (11) Paper Processing and (12) Milk Processing.

2.3. Micro, Small and Medium Enterprises

The existence of MSMEs has been proven to be the savior of economies in times of crisis. It is therefore not surprising that the Indonesian government pays particular attention to various supporting and assisting measures. However, in small business literature a common theme is the issue of resource constraints relating to time, expertise, finance or labor [14]. On the other hand, some researchers [15,16] argue that one of the greatest strengths of a small business stems is from strategic decision making which is not inhibited by the inherent bureaucracy of a managerial hierarchy which gives it the potential capacity to respond quickly the market change. The research uses the classification of MSMEs as stated in Law Number 7, Year 2021 regarding Ease, Protection, and Empowerment of Cooperatives and Micro, Small and Medium Enterprises.

3. Research Method

3.1. Research Model

As several previous researchers have explained, MSMEs implement a different marketing approach from the large companies. This is due to the uniqueness and various limitations faced by MSMEs. The marketing capabilities of small companies will be improved through entrepreneurship that are relevant to the concept of entrepreneurial marketing [8,10]. Through entrepreneurial marketing capabilities such as always adjusting to customer demand, innovation orientation and focus on customer relationships naturally, it is thought to be an added value for MSMEs in developing their business. Along with increasing business development quantitatively (profit, sales, assets) and qualitatively (learning ability, penetration ability), the entrepreneurial marketing exhibits a positive impact on MSME performance, competitiveness and preparedness in facing the AEC [6,17]. Especially in developing countries, the MSMEs certainly cannot be separated from the role of government, which is the main determinant factor for sustainability [18]. Based on some research in the literature, this study attempts to formulate the development model of MSMEs in facing the AEC. We propose a modified model by including the AEC readiness variables which constitutes the novelty of this research (Figure 1).

3.2. Data Collection

This research has been conducted at three industrial clusters in two provinces in Indonesia, well-known as the centers of agro-based industry clusters: Fruit and Fish Processing Clusters in Bandung District of West Java Province and Dairy Industry in Pekalongan District of Central Java Province. Primary data collection was carried out in 2016 in accordance with the MEA agreement. The research sample includes leading SMEs in the fruit processing industry cluster, the fish processing industry cluster and the dairy processing industry cluster. The total number of respondents obtained is 102 owners of small and medium-sized agro-industries. After cleaning the data, the data that can be processed further are 93 business actors representing leading MSMEs in the fruit processing industry cluster, the fish processing industry cluster and the milk processing industry cluster. This number is considered sufficient for the Structural Equation Modeling analysis. Primary data collection in this study was conducted through structured interviews using close ended questionnaires. The data is then grouped according to the subjected variables. This study also used secondary data sets, obtained either from respondents of MSMEs, or from their stakeholders in the institutions or parties concerned.

3.3. Data Analysis

Descriptive analysis was used to elucidate the frequency distribution of the research variables such as the characteristics of the MSMEs and the characteristics of the business actors. To develop a model of enhancement for the agro-industry clusters, this research employed Structural Equation Modelling (SEM) with a Partial Least Squares (PLS) approach. Smart partial least squares structural equation modelling (Smart PLS-SEM) allows for the estimation of multiple relationships, simultaneously considers both latent and observed variables, and takes into consideration the bias effects of random measurement error in the latent variables [19]. The first stage presented the outer model measurement that defines how the indicator blocks correspond to the latent variables. The construct was considered to be high if it has a loading factor greater than 0.7 [20]. At the next stage, the structural or inner model described the significance of each relationship and the influence of latent variables. The process of obtaining the inner model was through a bootstrapping technique—a technique of random sample data recalculation in obtaining the t-statistic values. Bootstrapping through Smart PLS indicates that the total of influence on the model showed a t-statistic value greater than t-table value of 1.96. In addition, this model measured R squared, which explains the Endogenous construction variability that can be explained by variability of exogenous constructs.

4. Results

4.1. Profile of the Agro-Industry SMEs Actors

The demographics of the agro-industry indicate that the business actors in the fish and dairy processing clusters are dominated by persons with an average of 41–50 years old. The business participants in the fish and dairy industry are those who have experienced and pioneered their own businesses. In contrast to the fruit-processing industry cluster, 55% of business actors are in the 21 to 30-year age category. These age ranges include the highly productive years that tend to have greater creativity and flexibility in undertaking business. This finding also corresponds to a field observation, namely that it is easier to enter and open a business in the fruit-processing industry cluster than the two other industrial processing clusters. Therefore, younger business entrepreneurs tend to be attracted to the fruit-processing clusters.

Based on the primary data, the level of education can be a supporting factor in terms of utilizing technological advances and marketing of MSMEs. Based on

Table 1. Profile of SMEs actors.

Category	Criteria	Fruit-Processing İndustry (%)	Fish Processing İndustry (%)	Dairy İndustry (%)
Age	21–30 years	55	23	13
	31–40 years	10	23	13
	41–50 years	29	42	52
	>50 years	6	13	23
Subtotal		100	100	100
Highest education	Did not finish school	0	0	3
	Elementary school	13	10	3
	Junior high school	36	26	19
	Senior high school	45	61	49
	University	6	3	26
Subtotal		100	100	100
Reason to start business	Own motivation	81	65	84
	Follow the parents	136	29	16
	Follow the relation		6	0
Subtotal		100	100	100
Seeking of business information	Never	10	10	13
	Rarely (min. Once a year)	19	35	26
	Often (min. Once a month)	58	42	48
	More often (more than once a month)	13	13	13
Subtotal		100	100	100

, most agro-industry-based business actors are dominated by workers who have had Senior High School education. Interestingly, while only 3–6% of those had degree from university preferred working in fruit and fish processing industry, 26% of workers in dairy/milk processing industry graduated from university.

From the results, it can be said that the business actors in the three agro-clusters started their business because of their own aptitudes and motivation. In addition to that, the results show that more respondents may start running the business following input from their parents than any other relation. It has been noted that no person stated following the relation to work in dairy industry, while only 6% of the workers in the remaining industries declared starting the business because of their relation. Business actors in these three major agro-based industry clusters frequently (once per month) search for business information from outside their direct environment. These results indicate that most business actors consider business information outside their immediate environment to be one of the factors which are important for business development.

4.2. Profile of the Agro-Industry SMEs Capability

The milk industry cluster is the longest standing business compared to the other two processing clusters (

Table 2. SMEs capabilities.

Category	Criteria	Fruit-Processing İndustry (%)	Fish Processing İndustry (%)	Dairy İndustry (%)
Length of business	Less than 5 years	42	55	23
	5–10 years	35	32	58
	More than 10 years	23	13	19
Subtotal		100	100	100
Turnover per month (US$)	1–707	74	68	61
	>707–1415	13	16	7
	>1415–2122	10	7	16
	>2122–2830	0	3	3
	>2830–3537	3	6	0
	>3537	0	0	13
Subtotal		100	100	100
Type of business	Household industry (1–4 people)	81	90	74
	Small industry (5–19 people)	16	7	19
	Medium industry (20–99 people)	3	3	7
Subtotal		100	100	100
Ownership of Production Equipment	Very inadequate	13	26	20
	İnadequate	58	64	58
	Adequate	26	10	19
	Very Adequate	3	0	3
Subtotal		100	100	100
Capital Ownership	Very inadequate	13	10	23
	İnadequate	74	80	58
	Adequate	13	10	19
	Very Adequate	0	0	0
Subtotal		100	100	100
Stability of personnel	Very inadequate	3	7	6
	İnadequate	62	58	58
	Adequate	32	32	36
	Very Adequate	3	3	0
Subtotal		100	100	100
Business Status as Source of Income	Not main income	3	3	0
	Additional family income	52	58	26
	Main income	35	36	61
	Very likely to become main income	10	3	13
Subtotal		100	100	100
Fulfillment of Household Needs	0–25%	19	26	16
	26–50%	26	48	29
	51–75%	45	23	48
	76–100%	10	3	7
Subtotal		100	100	100
Other Business Ownership	none	42	42	74
	1 other	58	48	10
	2 others	0	10	16
	3 others	0	0	0
Subtotal		100	100	100

). The cluster for milk is dominated by respondents with 5–10 years of business experience (58%), whereas the fruit and fish industries are dominated by respondents with less than 5 years’ experience (2% for fruit-processing cluster and 55 percent for fish processing cluster). The three clusters generate monthly turnover in the range of US$.71 to US$.701. However, the turnover is less than what is generated by other micro level businesses (US$ 1768 per month). In accordance with the level of monthly turnover achieved by each cluster, the majority of which are in the category of micro-enterprises, the three agro-industries selected fall into the group of small family businesses (1–4 people), with 81% in the fruit-processing cluster, 74% in the milk cluster, and 90% in fish clusters. As with the problems faced by MSMEs in general, the same problems also occur in these three industries. The restrictions include ownership of equipment, raising capital, and managing human resources These results are consistent with previous research that showing that the SMEs are often constrained by operational constraints, namely difficulties in obtaining equity or debt financing [21]. These factors contribute to the competitive challenges faced by MSMEs in agro-industrial processing clusters.

Significantly, 58% of actors in the fruit-processing cluster and 48% in the fish processing cluster have at least one other business. It has been noted that, 52% of fruit-processing-cluster businessmen and 58% of fish businessmen consider their businesses as simply additional family income. In contrast, most of the business owners in dairy agro-industry-cluster have no other business activities (74%). Therefore, it is understandable that 61% of businesses in the milk cluster reported that this business was the main source of family income. In terms of fulfilling family needs, the majority of businesses acknowledged that their businesses were only able to fulfill their household needs by 51–75%. This illustrates that their business urgently require development to provide the necessary support to cover all their household needs.

4.3. Analysis of Structural Equation Model (SEM)

Tests were carried out on the entire SEM model through the goodness of fit test with the R-square statistical indicator. Other match indices such as loading factor, composite reliability, Average Variance Extracted (AVE), Square Root AVE, and Cross Loading have met the cutoff value (see

Table 3. Final model goodness of fit test results.

No.	Criteria	Cutoff Value	Results	Note
1.	R-square of endogenous latent variables	Chin (1998) classifies R2 values of 0.67; 0.33; and 0.19 as “substantial”, “moderate” and “weak”	Competitiveness = 0.419	Fit
			Entrepreneurial marketing = 0.204
			AEC readiness = 0.377
			Business development = 0.450
2.	Loading factor	≥0.7	All indicators have a loading factor ≥ 0.7	Fit
3.	Composite Reliability	>0.6	Competitiveness = 0.904	Fit
			Entrepreneurial marketing = 0.855
			Government support = 0.923
			AEC readiness = 0.933
			Business development = 0.904
4.	Average Variance Extracted (AVE)	>0.5	Competitiveness = 0.759	Fit
			Entrepreneurial marketing = 0.542
			Government support = 0.599
			AEC readiness = 0.736
			Business development = 0.573
5.	Square Root AVE	Greater than the value of correlation between variables	All AVE square root values of latent variables are greater than other latent variables	Fit
6.	Cross Loading	Each indicator has a higher loading for each latent measured, compared to other latent indicators	All construct variables have a greater correlation on latency alone than correlation to other latencies	Fit

). Thus, the overall conformity index is considered acceptable, and the model is good in testing the data [21].

The strength of the construct variable can be seen from the value of the loading factor of each indicator, as shown in the SEM Agro-Based Industry Measurements in the

Table 4. Measurement items.

Construct	Item Used	Factor Loading
Entrepreneurial Marketing	1.1.3.06 The level of ability to develop existing products	0.777
	1.1.3.08 The level of ability to be creative in sales	0.71
	1.1.3.09 The intensity level of the application of product diversification	0.725
	1.1.3.10 The level of diversity of the types of products currently available	0.757
	1.1.3.11 The frequency level follows the tastes of customers in the market	0.711
Government support	1.1.4.04 The level of frequency the government organizes marketing and sales training	0.752
	1.1.4.05 The level of frequency the government organizes product production/processing training	0.773
	1.1.4.06 The level of frequency the government organizes financial training	0.809
	1.1.4.07 Frequency level for equipment assistance from the government	0.808
	1.1.4.08 Frequency level of facility (market) for small businesses by the government	0.747
	1.1.4.09 The level of frequency of competition control among the SMEs by the government	0.782
	1.1.4.10 The level of frequency control of competition between SMEs with large businesses by the government	0.773
	1.1.4.11 The level of government frequency in providing easy licensing procedures for MSMEs	0.744
Business development	1.1.5.03 The level of ability to invest in other additional factors of production such as adding equipment and employees	0.731
	1.1.5.04 The rate of profitability of the product every year	0.734
	1.1.5.06 Growth rate of employment per year	0.726
	1.1.5.08 The growth rate of assets in the form of current assets such as cash, receivables or inventories	0.799
	1.1.5.09 The growth rate of assets in the form of fixed assets such as land, buildings, machinery or equipment	0.789
	1.1.5.10 The growth rate of assets in the form of intangible assets such as patents, copyrights or rental rights	0.743
	1.1.5.11 The growth rate of assets in the form of long-term investment	0.772
Competitiveness	1.1.6.01 Level of ability to compete with other similar businesses in the City/Regency	0.874
	1.1.6.02 Level of ability to compete with other similar businesses in the province	0.933
	1.1.6.03 The rate of increase in the number of customers each year	0.802
AEC readines	1.1.7.02 The level of readiness of business actors in facing the arrival of competing products from ASEAN countries	0.759
	1.1.7.03 The level of competitiveness of human resources possessed to produce products according to MEA standards	0.88
	1.1.7.04 The level of ability to deal with the arrival of similar products originating from ASEAN countries	0.91
	1.1.7.05 The level of ability to face the arrival of entrepreneurs from ASEAN who opened similar factories such as the business of the father and mother	0.854
	1.1.7.06 The level of ability to cooperate in terms of capital with employers in ASEAN countries	0.879

.

Hypotheses Testing

To determine the significance of the influence of each construct, hypothesis tests were applied. The link between exogenous and endogenous variables is shown in

Table 5. Total effects: path estimates, significance, hypothesis testing.

Endogenous Variable	β	t Value	Decision
H1.Government support → Business development	0.509	7.414	Supported
H2.Government support → Entrepreneurial marketing	0.452	5.922	Supported
H3.Entrepreneurial marketing → Business development	0.264	3.012	Supported
H4.Business development → Competitiveness	0.412	4.271	Supported
H5.Entrepreneurial marketing → Competitiveness	0.336	3.367	Supported
H6.Competitiveness → AEC Readiness	0.614	7.784	Supported

β = standardized path coefficient.

. If the t value is >1.96, it can be approved that the two variables are significantly interrelated. Meanwhile, the strength of the relationship between constructs can be seen from the path coefficient.

5. Discussion

The entrepreneurial marketing latent variables can be indicated by the five reflective indicators; (1) the ability to develop existing products; (2) the ability to be creative in sales, (3) product diversification, (4) product availability, and (5) the tastes of customer in the market. From the analysis, it has been noted that, the highest reflective indicators reflect the ability of a business to develop existing products (item 1.1.3.06). These results indicate that product development is the dominant factor in entrepreneurial marketing in the agro-industry-based cluster development model, with factor loading value of 0.777. Product development capabilities include the ‘conceptual’ indicators in the entrepreneurial marketing latent variable. The model indicates the needs of the entrepreneurial marketing to focus on innovation orientation and the existence of ideas and intuition in assessing the market needs. The ability of the MSME sector to innovate has helped them to adapt and survive in their business development even in the recent economic crisis and recession [22,23,24]. The SEM Modelling also shows the importance of product innovation to succeed in facing the AEC challenges.

Constructive variables of the Government support are indicated by eight indicators; (1) the level of frequency the government organizes marketing and sales training, (2) product production/processing training, (3) financial training, (4) equipment assistance, (5) facility (market) for small business, (6) competition control among the SMEs, (7) competition control between the SMEs and large businesses, and (8) the procedures to provide the MSMEs the licenses. From the results, it has been noted that the business actors need more financial training (item 1.1.4.06) in order to face the AEC marketing challenge with a loading factor value of 0.809. Financial training becomes a much-needed skill since it will impact in improving the financial health of businesses, especially their capitalization. By using the R-A theory, it is found that access to financial support positively improves SME performance [25]. Government support, especially in the financial aspect, can help the survival and long-term growth of SMEs because it enables them to engage in entrepreneurial initiatives in the pursuit of profitability. Based on the social learning theory, policies that relate to developing the MSMEs’ beliefs, attitudes and behavior, are required. This means that the government should facilitate MSMEs by emphasizing the internal learning process [26]. Good financial management can provide investor information on the potential for possible business growth. Thus, the limitations of capital can be overcome and eventually the business is more likely to be able to compete with similar industries from other AEC countries.

In this study, we highlight 7 important indicators in developing business among the SMEs; (1) the ability to invest in the production, (2) the annual profit rate, (3) the rate of employment growth, and the growth rate of assets in form of (4) current assets, (5) fixed assets, (6) intangible assets, and (7) the long-term investment. By the data analysis, it has been noted that the rate of fixed assets’ growth (item 1.1.5.08) is the most important factors of business development for small and medium business, with a factor loading value of 0.799. These indicators include the cashes, accounts receivable and inventories. To be ready for the highly competitive threat from ASEAN Economic Community (AEC), the SMEs may also consider other aspects to perform better development for their business, including the sales growth, market share and overall performance [27].

To increase the SMEs Competitiveness in facing the AEC, the agro-based industries should consider 3 important factors: the ability to compete with similar business in (1) the city/regency, and (2) the province, and also (3) the rate of customer number each year. From the Cluster Model, it has been noted that the ability of SMEs to compete with other similar business (item 1.1.6.02, loading value of 0.933) in the province is considered the most important factor to grow during the AEC. The ability to compete within the province can be the first step to face the challenges of competing with similar products from other AEC countries.

The preparedness of the SEMs agro-based industry for facing the AEC is indicate by five important indicators; (1) the readiness to compete with products from other ASEAN countries, (2) the human resources competitiveness to produce products following the AEC standards, (3) the ability to deal with similar products from other ASEAN Countries, (4) the ability to compete with entrepreneurs or business player from other ASEAN Countries, and (5) the ability to cooperate with businesses from other ASEAN Countries. Among them, it has been noted the ability to compete with similar products from other ASEAN Countries (item 1.1.7.04) is a really important indicator to make the SMEs ready with the AEC implementation (loading factor 0.910). The ability of businesses to understand the implications of product flow is fundamental in facing the challenges of the AEC. Agro-cluster business actors can start by increasing their entrepreneurial marketing ability, including the product innovation, in order to face the potential product competition from ASEAN countries. Previous studies [28,29] have confirmed the significant effect of the entrepreneurial orientation on the business internationalization and its performance.

Governmental support is required to improve the industry. This support is reflected in the direct influence of government support, or policy, on entrepreneurial marketing ability with a coefficient value of 0.452 (H2: Government support → Entrepreneurial marketing). As described earlier, product innovation is the most dominant indicator in reflecting entrepreneurial marketing. Therefore, government support can also be directed to improve the ability of SMEs’ actors. Entrepreneurial marketing such as innovation activities in the SMEs is influenced by internal factors, including the role of leaders, organizational culture and employee abilities [30] as well as external factors such as the innovation ecosystem, government support and consumer demand [31,32]. In addition, government support also has a direct effect on business development with a coefficient value of 0.509 (H1: Government support → Business development). This research shows that government support shows a major contribution to the progress and productivity of SMEs. This result denotes that in developing countries, government support both financial and non-financial shows a major impact on improving the ability of innovation, risk taking, and competitiveness of SMEs.

In addition, the results of this study also show that the personal abilities of business actors described in the context of entrepreneurial marketing are actually able to create business development (H3: Entrepreneurial marketing → Business development) and increase business competitiveness (H5: Entrepreneurial marketing → Competitiveness). Entrepreneurial marketing such as innovations in small and medium-sized business drives marketing performance positively [33]. The marketing performance is a vital component of overall business performance, including SMEs [34] because it has a direct impact on profitability [35].

When a business is at a stage where it is being run well, then that business will be more likely to be competitive within its respective industry. This is shown in the results by a significant direct influence of business development variables on competitiveness through a relatively high coefficient value of 0.412 (H4: Business development → Competitiveness). Finally, good competitiveness will increase the readiness of business actors to face the challenges of the AEC. The SMEs with the competitive edge would enjoy the benefits from the establishment of the AEC due to their readiness and ability to penetrate and internationalize business into other ASEAN markets [36]. This was observed by a coefficient value of 0.614 (H6: Competitiveness → AEC Readiness). When entrepreneurs in agro-based industry clusters are able to compete with each other, at least in the local market (at provincial level), and are able to maintain their domestic market share, then they are more likely to be prepared for the challenges when facing the AEC. The competitiveness of SMEs can be reflected from the level of learning capability and integration capability that generate a positive impact on the performance of SMEs and readiness in facing the AEC. Providing the SMEs with the technical assistance, training and regional trade exports facilitation, exports financing schemes, market information, and ease of supply of raw materials would promote the SMEs to move toward a level of AEC readiness [36,37].

6. Conclusions

Most of the business owners in the subjected small and medium enterprises started the business by their own motivation. This study indicated that young entrepreneurs own business in the fruit-processing industry. Education is not important factor to run a business in the agro-industry. However, the business owners often seek updated business information to grow and develop their business. MSMEs agro-based-industry-cluster profiles are dominated by businesses which have existed for less than five years. Only milk processing clusters have been operating for 5–10 years and are considered as small enterprises. The MSMEs experience various obstacles in equipment and human resources and run as family businesses. MSMEs fruit-processing industry, fish processing industry and dairy industry still make their business the main family income. Thus, there is a need to improve their business productivity to live in financial security from their business.

From the SEM Agro-Based Industry Cluster Model, we have highlighted the most important factors that may help the MSMEs face the challenges in AEC implementation. Entrepreneurial marketing has significant impact in increasing business development and business competitiveness. The business actors should improve their entrepreneurial marketing abilities in terms of innovativeness. The findings also emphasize the importance to pay attention to product development and innovation in developing and empowering MSMEs industries. Moreover, it is necessary for the government to facilitate financial training and product innovation for the business actors. Various government policies should stimulate the growth of business assets and market access for MSMEs to face the AEC implementation.

This research has also provided a number of vital contributions. Through a structured model, testing has been carried out to examine the effect among several determinant factors of SMEs readiness in facing AEC implementation. Moreover, theoretically, the study is one of the very few studies conducted in the context of a developing country in the micro, small and medium-sized agro-industry. Previous studies examined innovation ability and entrepreneurial self-efficacy separately. However, in this study the measurements were carried out simultaneously with a complex model.

In addition, there are several recommendations to be considered from this research. The concept of entrepreneurial marketing skills can be developed by integrating market orientation, entrepreneurial self-efficacy or entrepreneurial orientation theories from the perspective of SME practitioners. Furthermore, the ability of digital marketing through social media can be an alternative, and also a new implementation strategy in facing the challenges of the AEC marketing environment in 2025.