Artificial intelligence learning and entrepreneurial performance among university students: evidence from malaysian higher educational institutions

Abstract

Artificial intelligence learning at higher educational institutions is one of the emerging concepts having vital importance to promote entrepreneurship activities among the university students. However, Malaysian Universities are lacking with the artificial intelligence learning activities. The objective of the study is to examine the role of artificial intelligence learning to promote entrepreneurship performance with the help of entrepreneurial orientation and strategic entrepreneurship. Moreover, the moderating role of government funding and attitude towards entrepreneurship is also examined. To achieve the objective of this study, a survey was carried out among the Malaysian universities. 500 questionnaires were distributed among the universities and data were collected from the teaching staff. After collection of data, it was analysed with the help of Partial Least Square (PLS)-Structural Equation Modeling (SEM). It is concluded that artificial intelligence learning is most significant to promote entrepreneurial performance among university students. Entrepreneurial orientation and strategic entrepreneurship play a key role to transfer the positive effect of artificial intelligence learning on entrepreneurial performance. Additionally, government funding and attitude towards entrepreneurship also has significant role.

Keywords

Artificial intelligence learning higher educational institutions strategic entrepreneurship government funding entrepreneurial orientation performance entrepreneurial attitude

1 Introduction

The expansion of artificial intelligence-based technologies in entrepreneurship is evolving quickly, however, real-world entrepreneurship application has not yet become a significant reality. There are various crucial practical issues surrounding the implementation of artificial intelligence in entrepreneurship. Management of issues and implementation of artificial intelligence in entrepreneurship is key to every business success. As the artificial intelligence has vital role in business [59], therefore, it has significant contribution to the entrepreneurship success.

Artificial intelligence can be defined as, “it is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.” It is based on various important elements such as machine leaning, artificial intelligence planning, robotics, artificial general, language processing, computer version, knowledge representation and reasoning. These elements contribute towards the technologies of artificial intelligence and proved to be most significant for business activities. Generally, artificial intelligence is based on latest technologies [26, 57]. Therefore, to implement artificial intelligence in entrepreneurship activities, it requires higher level of new technology implementation. Latest technology is lacking in entrepreneurship activities, that is why the artificial intelligence practices are lacking. It is also important because in entrepreneurship success, the role of latest technology is most important [66].

Therefore, it is important to implement artificial intelligence in entrepreneurship activities to increase the performance. Increase in entrepreneurship performance is most important for every country. As the entrepreneurship activities has important role in economic development [6]. Importance of entrepreneurship is not only limited to the nation, it is also much important for the economic development of facilities and communities. Increase in the economic development of families and communities directly lead to increase in the nation’s economic development. Therefore, entrepreneurship has central importance for the country. Thus, it is needed to promote entrepreneurship activities by using artificial intelligence from basic level. In this way, artificial intelligence learning is key to implement these technologies. Students at university level must learn artificial intelligence technologies to boost entrepreneurship activities.

In this case, artificial intelligence learning at higher educational institutions like universities is most important. Particularly, Malaysian universities have extensive need to implement artificial intelligence learning. Because the Malaysian universities are lacking with artificial intelligence learning for students. It is important to have entrepreneurship artificial intelligence learning in universities of Malaysia because entrepreneurship has significant contribution to Malaysian economic growth which is possible through university students entrepreneurship intention [45].

Artificial intelligence learning at university level is important to develop student entrepreneurship orientation. As the entrepreneurial learning always lead to the entrepreneurial orientation. Learning orientation has significant role to promote entrepreneurship activities [39]. From very basic level, the students can learn the artificial intelligence advantages, technologies and benefits for entrepreneurship. After the academic career, these students can implement new technologies for the growth of entrepreneurship. Moreover, entrepreneurship orientation lead to the various strategic entrepreneurship activities. It shows positive effect to promote strategic entrepreneurship. Strategic entrepreneurship involves; exploration and exploitation. Exploration and exploitation both have key consideration in entrepreneurship activities (36) which lead to the better performance of entrepreneurship.

Along with entrepreneurship learning and strategic entrepreneurship, government funding is important to promote artificial intelligence learning in Malaysian universities. Artificial intelligence learning services in universities requires significant financial cost which is quite hard for universities to bear. In that case government funding is important for universities. It is also described by the previous studies that government funding has important role in entrepreneurship [29]. Therefore, funding from government can foster the artificial intelligence learning for entrepreneurship among the Malaysian universities. In the current study, government funding is considered as the moderating variable between artificial intelligence learning and entrepreneurial orientation.

Additionally, with all other elements, attitude towards entrepreneurship is also most important. If an individual has lacking with the attitude towards entrepreneurship, then it is not possible to get success in entrepreneurship venture. Positive attitude towards entrepreneurship is also based on the positive culture in the educational institutions and culture play a crucial role in entrepreneurship. Lacking in attitude towards entrepreneurship decreases the level of dedication which effect on the success. It is proved by the previous studies that attitude has key role in entrepreneurship activities [2]. In the current study, attitude is considered as moderating variable between strategic entrepreneurship and entrepreneurial performance.

The objective of the study is to examine the role of artificial intelligence learning to promote entrepre-neurship performance with the help of entrepreneu-rial orientation and strategic entrepreneurship. Moreover, the moderating role of government funding and attitude towards entrepreneurship is also examined. Figure 1 shows the relationship between artificial intelligence learning, entrepreneurial orientation, strategic entrepreneurship, government funding, attitude and entrepreneurial performance.

Fig. 1

Theoretical framework of the study showing the relationship between artificial intelligence, entrepreneurial orientation, strategic entrepreneurship, government funding, attitude and entrepreneurial performance.

The current study has key contribution to the literature, as this is one of the pioneer studies which introduced the concept of artificial intelligence learning among the students of higher education. Various studies introduced entrepreneurial orientation and entrepreneurship performance among education institutions [11 , 13]. However, studies are missing with the role of artificial intelligence.

2 Related work

Entrepreneur can be defined as person who are innovative and creative in driving something from the existing part to next level. It also means to upgrade something to be more meaningful and benefit for the society. Beside entrepreneur role can be described as an individual who undertakes responsibility as well as ownership in making various things happen; is able to bring innovation; who achieves the risks attached to the procedure; and who has the determination to see through some recognised end-point, even when faced with difficulties. Entrepreneurs also defined as someone who can make decision and responsible for any decision made. Sometime entrepreneur fails but the experience and the mistake make them learn more in future. Entrepreneurship is a dynamic process of vision, change and creative that requires an application of energy and passion toward the creation and implementation of new idea and creative solutions. To support the entrepreneurship in the country, the successful collaboration between government, education and institution must be the pillar of the successful plan [30, 32].

An entrepreneurial society’ can be developed within the education or living in the education institutional time. It may encourage type of creativity employed in entrepreneurial activity. Educational time can encourage the entrepreneurship as career choices. Technical and business can be developed in this stage [54] which can help to foster the economic activity like entrepreneurship. Therefore, at university level, student should learn various activities related to the entrepreneurship.

Nowadays, entrepreneurship has vital importance for the businesses. Entrepreneurship is one of the key elements emphasised by the government of Malaysia. Small enterprises always have been given priority by the government and received huge allocated amount for the development of entrepreneurship circle among Malaysian students. Parallel with government objectives to increase the number of entrepreneurs, many parties such as university, bank and club committee have involved directly and indirectly to make success of the government’s plan. University may also assist the government policy in terms of the teaching subject, curriculum and method design used and how the teaching material gives the impact towards entrepreneurship [23, 55]. Particularly, the universities in Malaysian requires significant support to entrepreneurship for young entrepreneur development.

Malaysia is known to be one of the successful development countries in this region. Thus, Malaysia is targeting a total of 5,000 young entrepreneurs trained each year. To reduce the rate of unemployment among graduates, the government encouraged the graduates to become entrepreneurs after graduation. For this initiative, the government has announced one soft loan funds of RM50 million under the Graduate Entrepreneur Fund managed by SME Bank. This fund will provide soft loans total of RM500 thousand with an interest rate of four percent. It is one of the steps to promote young entrepreneurs.

Therefore, it is part of university roles to prepare the students towards utilising the fund allocate from the government budget to upgrade youth standard and increase personal and socio-economic living among student after completing the studies. The university’s roles such as entrepreneurial intention, the student readiness in entrepreneurship, the impact of education program towards entrepreneurship and many more that try to indicate the clear picture on factors contributing to intention of being or act as entrepreneur. Is there are so many factors been identified? According to demographical character such as age, gender and level of qualification give the conclusion that man in age 35–40 are the most typical person in starting a new venture. The maturity level also been discussed widely [23]. Therefore, university has important role in student entrepreneurship activities [32, 38] to build various skills.

Additionally, government funding has important role in entrepreneurship performance [56]. Furthermore, the assigned fund for various activities of entrepreneurship should be fully used to grab the opportunity surrounding as well as to upgrade social living status. In whole this process, universities have most important role to deliver the strategies for entrepreneurship. Better strategies suggestion to the government has the ability to develop strategic plan for entrepreneurship activities.

Besides, the other factors that been widely identified are the personal entrepreneurial traits such as risk taking, creative, innovative, family background and quick decision making. Some experts emphasized the personal characteristics of the individual including idea about the need for achievement and focus of control on innovativeness. Moving forwards now society starts identifying the role of educational institutional study that can help contributes to identify the talented young brilliant student to create intention since study at university or college [47]. Apart from other factors, artificial intelligence is one of the factors which effect on entrepreneurship activities of the university students.

The good outcomes of entrepreneurial education have been evaluated by both researchers and educators; nevertheless, the impacts of these programs have on entrepreneurial competencies and the intention to become an entrepreneur has remained largely unexplored. Therefore, universities should focus to explore the hidden capabilities of students for entrepreneurship. Meanwhile, building a new venture is also building people and entrepreneurship needs people that can overcome hurdles. Calm in solving problem and eagle completing job without delay. They must be discipline, tenacious, persistent, able to commit and recommit effectively and the most important thing is they are not intimidated by challenges [10].

In line with the building new venture, previous research explores the five common indicators that specify as the encouragement factor on someone to become an entrepreneur. These are early childhood experiences, the need to gain control over an uncertain world, frustration with traditional organizational careers, challenge and excitement; and the moral encouragement of role models. Most of the entrepreneurs that have high income are the young university or college graduates. Most of them are exclusively opportunity-motivated to start-up a new venture or business. These also called “necessity entrepreneurs” [9]. However, the current study introduced a new concept of artificial intelligence learning for university students. Artificial intelligence learning also effect on the student’s entrepreneurship activities.

Artificial intelligence is the simulation of human intelligence procedures through which machines, particularly computer systems. These procedures comprise learning (the procurement of information as well as rules for utilizing the data/information), reasoning (utilizing rules to reach estimated or certain conclusions) and self-correction. In the current decade, these practices of artificial intelligence are increasing among the companies. These practises are also increasing among the entrepreneurship activities [18]. Entrepreneurship activities are moving towards the adoption of higher technology which lead to artificial intelligence practices. These practices have important role to increase the business performance among entrepreneur because of latest technology implementation. According to the context of current study, these practices should also be included among the universities. Students should learn the artificial intelligence learning practices in universities which will lead to increase in the motivation of students towards entrepreneurship. Therefore, artificial intelligence learning is most important to introduce among the Malaysian universities to boost the entrepreneurship performance.

Artificial intelligence learning is the process to teach various technologies such as robotics, machine learning, artificial intelligence planning, language processing, artificial general intelligence, knowledge representation and reasoning. Therefore, it is most important to highlight at university level because the young and educated entrepreneurs are more willing to introduce latest technologies in their businesses. Artificial intelligence has been getting increasing attention as well as concern in a diversity of research and application areas over the last few years, however, it has not attained much scrutiny in entrepreneurship research activities, particularly the entrepreneurship activities at university level. Only few studies are available which highlighted the artificial intelligence learning at education sector [49]. These studies are only limited to the artificial intelligence; however, the artificial intelligence learning is still missing which is covered by the current study. This study introduced artificial intelligence learning as one of the unique factors which influence the student entrepreneur’s performance at university level. It is one of the important needs of this competitive environment. Competition is increasing day by day through latest entrepreneurship technologies [51, 53] which requires artificial intelligence learning.

Artificial intelligence learning is important for young entrepreneurs at university level because it increase the interest on students towards entrepre-neurship which develop entrepreneurial orientation. Entrepreneurial orientation is one of the firm-level strategic orientation which captures an organization’s strategy-making practices, managerial philosophies, and firm behaviours that are entrepreneurial in nature. Entrepreneurial orientation has most important role in entrepreneurship activities [16, 29]. Artificial intelligence learning in educational institutions promote entrepreneurial orientation by supporting strategy-making practices. Entrepreneurial orientation is also important because it is one of the ways to shape firm behaviours towards entrepreneurship activities and ultimately increases the performance of business. Therefore, entrepreneurial orientation has the most important role to shape up the entrepreneurial activities and increases the business performance [53, 57]. Therefore, from the previous studies it is revealed that entrepreneurial orientation has most significant role in business performance. Additionally, artificial intelligence learning has relationship with entrepreneurial orientation which effect positively on entrepreneurial performance. Therefore, below hypothesis is proposed;

H1: Artificial intelligence learning has positive effect on entrepreneurial orientation.

Entrepreneurial orientation also has important role for strategic entrepreneurship activities. It effects positively on strategic entrepreneurship activities. The current study considered two strategic entrepreneurship activities including; exploration and exploitation [33]. Strategic entrepreneurship is defined as “the integration of entrepreneurial (i.e. opportunity-seeking behaviour) and strategic management (advantage-seeking behaviour) perspectives in developing and taking actions designed to create wealth” [28]. Both procedures are in exploration as well as exploitation actions which are aggressive but must be in equilibrium [55].

[42] described that exploration is concentration on the lookout for innovative opportunities, investigating and variation. It is defined as the things taken by terms like search, variation, various risk-taking actions, investigation, flexibility, discovery as well as innovation [42, 63], exploitation is concentration on the implementation of the extant opportunities, professionally as well as selectively. It is delineated as “such things as refinement, efficiency, choice, production, implementation, selection, execution. Both exploration and exploitation has important role in entrepreneurship activities [19]. Various studies in the literature proved that strategic entrepreneurship lead to increase in the business performance [26, 35]. Therefore, the Malaysian universities must focus on strategic entrepreneurship which should include exploration and exploitation strategies.

Exploration and exploitation are linked with the performance of entrepreneurship through entrepreneurial orientation practices. Entrepreneurial orientation has significant relationship with both the strategic entrepreneurship elements, namely; exploration and exploitation [15]. Significant relationship between entrepreneurial orientation, exploration and exploitation has important role to enhance entrepreneurial performance. Along with the indirect effect of exploration and exploitation, both have also direct effect on entrepreneurship performance [16]. Therefore, strategic entrepreneurship activities have important role to boost entrepreneurship performance with the help of entrepreneurial orientations.

H2: Entrepreneurial orientation has positive effect on exploration.

H3: Entrepreneurial orientation has positive effect on exploitation.

Furthermore, according to literature, strategic entrepreneurship has important role to enhance entrepreneurship performance. These strategies are important to manage the operations. Entrepreneurship performance is one of the important parts of enterprise which should be managed to get success. Motivated students always work in a team and work on exploration which lead to the success of overall business activity [46 , 58]. Therefore, it is important to introduce exploration activity of strategic entrepreneurship to expediate the entrepreneurship performance among the university students.

Along with the exploration, exploitation also has important role to enhance the entrepreneurship performance. It is evident from the prior studies that exploitation has significant role in performance [12 , 24]. The combined actions in exploration and exploitation of opportunity is somehow shifting the entrepreneurship in the direction of strategic management which shows positive effect on entrepreneurial performance. Additionally, innovation activities are most important for performance [62] which can be achieved with the help of exploration and exploitation. Therefore, from the above discussion, it is evident that artificial intelligence learning has positive role to enhance entrepreneurial orientation and entrepreneurial orientation promote strategic entrepreneurship (exploration, exploitation) and finally, strategic entrepreneurship increases the entrepreneurial performance. Therefore, following hypotheses are proposed;

H4: Exploration has positive effect on entrepre-neurial performance.

H5: Exploitation has positive effect on entrepre-neurial performance.

Government in this study refers to the country policy maker on the entrepreneurship. According to literature, it is mentioned that there are three types of governmental roles that can be study such as an invisible hand, a helping hand, or a grabbing hand. Based on the invisible hand approach, government will leave all decision to the market but provides basic public goods and services. The support from government can be list out as to provide financial support, create enabling business climate, strengthen organisational and managerial capacity of current and prospect entrepreneurs, narrow the gap among entrepreneurs and established business incubator [5].

Too many brilliant students are the outcome from the development of awareness towards important of educational level for our new generation. However, how many job and potential suitable position is available among government no matter the federal or state agencies waiting for all this brilliant student. Aware of this important fact and situation, the Malaysian government is ready with all those fund structure for new brilliant students to start their new life with something not disappointed. The government plan to decrease the unemployment rate from time to time and entrepreneurship fund is one of the government’s plans to overcome the situation.

The government schemes offered to enhance the interest, but sometimes it did not work as much as their hope due to personal attitude or inner thinking. The study of attitude and factors towards entrepreneurship help the university, community, society and government to reinforce the strategy to creating more intend and interest towards entrepreneurship as a career. Goal of government is to set a platform to achieve the goal on pursuing the achievement towards having the enhancement of social income and economic structure [7, 50] with the help of funding.

By having the high commitment in entrepreneurship, Malaysia is helping to positively contribute the said goal and objective set. There are so many agencies waiting to contribute their help on new venture set up fund such as Small and Medium Entrepreneurship Grant, Government Financial Institution Loan Scheme, Bank Negara Loan, and MITI Financial Assistant for small business, Loan and Investment Franchise Scheme and MARA Funded Scheme for Bumiputera Entrepreneurship. Meanwhile, in budget 2014, Malaysia government has announced that RM14 million has been allocated for entrepreneurship development to increase the development of entrepreneurship activities.

In addition, the main plan for SME in Malaysia is to enhance the contribution towards the economic development for the country. In year 2012, government has announced the Pelan Induk PKS (2012–2020). Data shows that the PKS has contribute 32% in the country and Malaysia hopes that by year 2020 the contribution should increase to 41%. Therefore, to achieve the plan government is committed to help PKS achieved the maximum potential. The report by the World Bank has shown that Malaysia improved the place from 18th place in year 2012 to 12th place in year 2013 for the best facilities provided for entrepreneurship. Business facilities 2013 for Asia Pacific Position is shown in Table 1. The statistic shown by the World bank proven that Malaysian Government has very serious on the development and enhancement of Malaysia entrepreneurship especially for young graduate student to fulfil the government objective [24].

Table 1
Business Facilities 2013, Asia Pacific Position (Government Business Facilities (Position)

Country 2011 2012 2013

Singapore 1 1 1

Hong Kong 2 2 2

New Zealand 3 3 3

Korea Selatan 15 8 8

Australia 11 15 10

Malaysia 23 18 12

Taiwan 24 25 16

Country	2011	2012	2013
Singapore	1	1	1
Hong Kong	2	2	2
New Zealand	3	3	3
Korea Selatan	15	8	8
Australia	11	15	10
Malaysia	23	18	12
Taiwan	24	25	16

Government roles and university role were not going to be one of the important factors towards entrepreneurship if the desire from the graduate is low. The desirability for self-employment can also be the pushing factor for the government and university role towards entrepreneurship. This is one of the key factors that might help the government objectives achievable. High desires drive the high commitment and interest towards attitude on entrepreneurship and attitude lead to entrepreneurial performance, particularly aggressive attitude lead to the financial losses [35 , 63].

H6: Government funding has positive effect on entrepreneurial orientation.

H7: Government funding moderates the relationship between artificial intelligence learning and entrepreneurial orientation.

The attitude toward entrepreneurship refers to the degree to which a person has a favourable or unfavourable evaluation or appraisal of the attitude in question. In general, the more favourable the attitude toward the entrepreneurship, then the stronger will be an individual’s intention to perform the entrepreneurship [9]. Under this operational term the question will be asking on opinion of student towards the entrepreneurship, entrepreneurship as a career choice, career prospect, interest on self-employment and entrepreneurship as the honourable profession.

Entrepreneurship seems to be the attractive career for young entrepreneur and university is the place whereby these young people can learn how to become the entrepreneur with correct knowledge and guidance. However, besides university roles, there are many factors more that can influence the attitude towards entrepreneurship such as the opportunities in society, government roles, environment and financial assistance [3]. Many factors such as family background, personality traits or behaviour have also been identified as the key influencer to create the deep interest of business venture and decide entrepreneurship as a career. One of the determinants studied in this research is the attitude. Attitude is one of the vital roles in entrepreneur if they want to become successful in this chosen career. However, from time to time, there are still studies focusing on the factors that can influence the young people interest of becoming the successful entrepreneur. The reason why students always be the target sample in their population is because the linkages between the target of the government and the implementation in the university level [2].

Deep understanding on the attitude is the most value contributing to entrepreneurship as a career that helps the university to identify the suitable scope. Besides, the society also plays an important role to communicate with university, the problem in their area which needs something new and up to date. The opportunity surrounding sometimes create most valuable density towards the initial start-up of interest towards entrepreneurship. Therefore, entrepreneurship activity by university students requires significant level of entrepreneurship attitude.

H8: Attitude towards entrepreneurship has positive effect on entrepreneurial performance.

H9: Attitude towards entrepreneurship moderates the relationship between exploration and entrepreneurial performance.

H10: Attitude towards entrepreneurship moderates the relationship between exploitation and entrepre-neurial performance.

3 Research methodology

Having identified the variables and developed the theoretical framework, the next step is to design the research in such a way to detremine the method, sample size, sampling, data collection and the requisite data can be gathered and analyzed to arrive at a solution. This study employed quantitative research approach because it is suitable to test the hypotheses. While using quantitative research approach, cross-sectional research design was selected.

A questionnaire survey was used in this study. Questionnaire was developed to collect the data and divided in to various sections based on the profile of respondents and research questions based on the key research variables, including; artificial intelligence learning, entrepreneurial orientation, exploration, exploitation, entrepreneurship performance, government funding and attitude towards entrepreneurship. Moreover, 7-point Likert scale was used in this study to collect the data. It is most appropriate scale to get response in more depth from the respondents.

Sample size was selected based on the recommendations of previous studies. [16] and [17] provide sample in a series for inferential statistics. “Sample having less than 50 participants will observed to be a weaker sample; sample of 100 size will be weak; 200 will be adequate; sample of 300 will be considered as good; 500 very good whereas 1000 will be excellent.” Based on these recommendations, this study used 500 sample size.

Traditionally, self-administered, postal or mail surveys have been often used and offer low-cost and easily administered results from a large number of respondents [33]. Nowadays, web-based or online survey becomes more popular in survey method because it provides short response time. However, both traditional and web-based surveys have their own advantages and disadvantages. This study used online survey to collect the data from Malaysian universities. Therefore, 500 questiioniares were distribued among the Malaysian universities. Data were collected from the teachers of these universities.

4 Data analysis and results

Data collected from the Malaysian universities was screen out to check the missing value, outlier in the data and normality of the data. All these factors can effect on the results of the results. Data screening out is shown in Table 2. It is found that data is free from all these issues. Moreover, while using the partial least square (PLS), non-normal data is no issue as PLS is suitable to handle non-normal data.

Table 2
Data screening out

No. Missing Mean Median Min Max SD Kurtosis Skewness

AIL1 1 0 4.954 5 1 7 1.849 –0.561 –0.677

AIL2 2 0 5.327 6 1 7 1.749 –0.474 –0.796

AIL3 3 0 5.244 6 1 7 1.829 –0.061 –0.966

AIL4 4 0 5.023 6 1 7 1.813 –0.669 –0.647

AIL5 5 0 5.018 5 1 7 1.874 –0.617 –0.674

AIL6 6 0 4.954 5 1 7 1.733 –0.723 –0.533

AIL7 7 0 4.733 5 1 7 1.93 –1.028 –0.41

EO1 8 0 4.889 5 1 7 1.785 –0.635 –0.616

EO2 9 0 4.788 5 1 7 1.799 –0.843 –0.334

EO3 10 0 4.986 5 1 7 1.863 –0.813 –0.535

EO4 11 0 4.76 5 1 7 1.834 –0.927 –0.333

EO5 12 0 5.171 6 1 7 1.805 –0.218 –0.878

EO6 13 0 5.134 6 1 7 1.805 –0.68 –0.66

EXPLR1 14 0 4.991 5 1 7 1.796 –0.439 –0.721

EXPLR2 15 0 4.926 5 1 7 1.797 –0.606 –0.637

EXPLR3 16 0 4.853 5 1 7 1.754 –0.532 –0.635

EXPLR4 17 0 3.963 4 1 7 1.915 –1.175 0.204

EXPLR5 18 0 5.221 6 1 7 1.725 0.048 –0.873

EXPOI1 19 0 5.184 6 1 7 1.745 –0.453 –0.705

EXPOI2 20 0 5.207 6 1 7 1.806 –0.164 –0.871

EXPOI3 21 0 5.203 6 1 7 1.753 –0.557 –0.731

EXPOI4 22 0 5.217 6 1 7 1.784 –0.128 –0.879

EXPOI5 23 0 5.203 6 1 7 1.743 –0.234 –0.798

EP1 24 0 4.995 6 1 7 1.855 –0.555 –0.708

EP2 25 0 4.028 4 1 7 1.866 –1.086 0.114

EP3 26 0 4.908 5 1 7 1.862 –0.77 –0.43

EP4 27 0 5.124 5 1 7 1.813 –0.593 –0.672

EP5 28 0 4.834 5 1 7 1.752 –0.839 –0.36

EP6 29 0 4.078 4 1 7 1.806 –1.102 0.071

EP7 30 0 5.037 5 1 7 1.701 –0.336 –0.624

GF1 31 0 5.009 5 1 7 1.712 –0.739 –0.475

GF2 32 0 5.037 5 1 7 1.788 –0.631 –0.597

GF3 33 0 5.018 5 1 7 1.778 –0.548 –0.568

GF4 34 0 5.18 5 1 7 1.731 –0.412 –0.683

GF5 35 0 4.876 5 1 7 1.912 –0.924 –0.538

AE1 36 0 4.843 5 1 7 1.978 –0.678 –0.699

AE2 37 0 3.963 4 1 7 1.946 –1.23 0.215

AE3 38 0 4.991 5 1 7 1.934 –0.596 –0.722

AE4 39 0 5 5 1 7 1.871 –0.678 –0.616

AE5 40 0 4.894 5 1 7 2.014 –0.84 –0.627

	No.	Mean	Median	Min	Max	SD	Kurtosis	Skewness
AIL1	1	4.954	5	1	7	1.849	–0.561	–0.677
AIL2	2	5.327	6	1	7	1.749	–0.474	–0.796
AIL3	3	5.244	6	1	7	1.829	–0.061	–0.966
AIL4	4	5.023	6	1	7	1.813	–0.669	–0.647
AIL5	5	5.018	5	1	7	1.874	–0.617	–0.674
AIL6	6	4.954	5	1	7	1.733	–0.723	–0.533
AIL7	7	4.733	5	1	7	1.93	–1.028	–0.41
EO1	8	4.889	5	1	7	1.785	–0.635	–0.616
EO2	9	4.788	5	1	7	1.799	–0.843	–0.334
EO3	10	4.986	5	1	7	1.863	–0.813	–0.535
EO4	11	4.76	5	1	7	1.834	–0.927	–0.333
EO5	12	5.171	6	1	7	1.805	–0.218	–0.878
EO6	13	5.134	6	1	7	1.805	–0.68	–0.66
EXPLR1	14	4.991	5	1	7	1.796	–0.439	–0.721
EXPLR2	15	4.926	5	1	7	1.797	–0.606	–0.637
EXPLR3	16	4.853	5	1	7	1.754	–0.532	–0.635
EXPLR4	17	3.963	4	1	7	1.915	–1.175	0.204
EXPLR5	18	5.221	6	1	7	1.725	0.048	–0.873
EXPOI1	19	5.184	6	1	7	1.745	–0.453	–0.705
EXPOI2	20	5.207	6	1	7	1.806	–0.164	–0.871
EXPOI3	21	5.203	6	1	7	1.753	–0.557	–0.731
EXPOI4	22	5.217	6	1	7	1.784	–0.128	–0.879
EXPOI5	23	5.203	6	1	7	1.743	–0.234	–0.798
EP1	24	4.995	6	1	7	1.855	–0.555	–0.708
EP2	25	4.028	4	1	7	1.866	–1.086	0.114
EP3	26	4.908	5	1	7	1.862	–0.77	–0.43
EP4	27	5.124	5	1	7	1.813	–0.593	–0.672
EP5	28	4.834	5	1	7	1.752	–0.839	–0.36
EP6	29	4.078	4	1	7	1.806	–1.102	0.071
EP7	30	5.037	5	1	7	1.701	–0.336	–0.624
GF1	31	5.009	5	1	7	1.712	–0.739	–0.475
GF2	32	5.037	5	1	7	1.788	–0.631	–0.597
GF3	33	5.018	5	1	7	1.778	–0.548	–0.568
GF4	34	5.18	5	1	7	1.731	–0.412	–0.683
GF5	35	4.876	5	1	7	1.912	–0.924	–0.538
AE1	36	4.843	5	1	7	1.978	–0.678	–0.699
AE2	37	3.963	4	1	7	1.946	–1.23	0.215
AE3	38	4.991	5	1	7	1.934	–0.596	–0.722
AE4	39	5	5	1	7	1.871	–0.678	–0.616
AE5	40	4.894	5	1	7	2.014	–0.84	–0.627

Partial Least Square (PLS)-Structural Equation Modeling (SEM) was employed to test the hypotheses. However, before testing hypotheses, data reliability and validity was examined by using PLS. Data reliability and validity was examined through PLS algorithm. PLS algorithm is shown in Fig. 2 and results are shown in Table 3, Table 4 and Table 5.

Fig. 2

Confirmatory factor analysis.

Table 3

Constuct reliability and valdidty

	Alpha	rho_A	CR	AVE
Artificial intelligence learning	0.955	0.956	0.963	0.789
Attitude towards entrepreneurship	0.924	0.926	0.946	0.813
Entrepreneurial orientation	0.943	0.944	0.955	0.779
Entrepreneurial performance	0.941	0.943	0.955	0.81
Exploitation	0.927	0.928	0.945	0.774
Exploration	0.884	0.886	0.921	0.745
Government funding	0.924	0.926	0.943	0.767

Table 4

AVE sqaure root

	AIL	AE	EO	EP	EXPOI	EXPLOR	GF
Artificial intelligence learning	0.888
Attitude towards entrepreneurship	0.617	0.902
Entrepreneurial orientation	0.822	0.602	0.883
Entrepreneurial performance	0.694	0.676	0.715	0.9
Exploitation	0.69	0.636	0.671	0.824	0.88
Exploration	0.806	0.661	0.809	0.811	0.801	0.863
Government funding	0.687	0.65	0.699	0.836	0.807	0.8	0.876

Table 5

Heterotrait-monotrait ratio

	AIL	AE	EO	EP	EXPLOI	EXPLOR
Artificial intelligence learning
Attitude towards entrepreneurship	0.652
Entrepreneurial orientation	0.77	0.641
Entrepreneurial performance	0.73	0.722	0.757
Exploitation	0.733	0.684	0.717	0.789
Exploration	0.784	0.729	0.772	0.792	0.79
Government funding	0.731	0.7	0.748	0.775	0.76	0.788

Table 3 shows the construct reliability and validity. It shows that all the construct have Cronbach alpha above 0.7, composite reliability (CR) is also above 0.7, factor loadings for all items is above 0.7 [54]. Factor loading is shown in appendix (Table 1). It is also shown in Fig. 2. Additionally, average variance extracted (AVE) is also above 0.5 which confirms the convergent validity.

Additionally, the discriminant validity is determined by using, AVE square root through [32] instructions, Heterotrait-Monotrait Ratio and cross-loadings. AVE square root and Heterotrait-Monotrait Ratio is shown in Table 4 and Table 5 respectively. In all methods, the current study fulfils the minimum criteria for discriminant validity. Cross-loadings is shown in appendix (Table 2).

After examining the construct reliability and validity, data was further analysed to examine the relationship between variables. For this purpose, PLS bootstrapping was used. It is shown in Fig. 3 and results are given in Table 6. It is found that artificial intelligence learning has positive effect on entrepreneurial orientation with t-value 17.079. It shows the artificial intelligence learning increases the entrepreneurial orientation. The effect of entrepreneurial orientation on exploration and exploitation also found significant with t-value 41.732 and 9.441, respectively. Therefore, increase in entrepreneurial orientation increases the exploration and exploitation which effect on business performance. Consistent with this, it is found that both exploration and exploitation have significant positive effect on entrepreneurial performance with t-value 2.406 and 11.857, respectively. Government funding also has significant positive effect on entrepreneurial orientation with t-value 2.507. It increases the entrepreneurial orientation among university students. Finally, attitude towards entrepreneurship shows positive role to boost entrepreneurial performance. As the relationship between attitude towards entrepreneurship and entrepreneurial performance found significant and positive with t-value 3.098. Therefore, H1, H2, H3, H4, H5, H6 and H8 are supported.

Fig. 3

Structural Model.

Table 6

Direct hypotheses results

	(O)	(M)	SD	T Statistics	P Values
Artificial intelligence learning ->Entrepreneurial orientation	0.837	0.829	0.049	17.079	0
Attitude towards entrepreneurship ->Entrepreneurial performance	0.111	0.114	0.036	3.098	0.002
Entrepreneurial orientation ->Exploitation	0.671	0.672	0.071	9.441	0
Entrepreneurial orientation ->Exploration	0.889	0.89	0.021	41.732	0
Exploitation ->Entrepreneurial performance	0.734	0.729	0.062	11.857	0
Exploration ->Entrepreneurial performance	0.15	0.152	0.062	2.406	0.017
Government funding ->Entrepreneurial orientation	0.124	0.132	0.049	2.507	0.012

Moderation effect is shown in Table 7. It is also shown in Fig. 4. PLS bootstrapping was used to examine the moderation effect. Three moderation effect was examined in the current study. The moderation effect of attitude towards entrepreneurship between exploration and entrepreneurial performance found significant with t-value 2.816. However, moderation effect of attitude towards entrepreneurship between exploitation and entrepreneurial performance found insignificant. Thus, H9 is supported, but H10 is not supported. The moderation effect of government funding between artificial intelligence learning and entrepreneurial orientation also found significant with t-value 2.626. These results supported H7. Additionally, all the indirect effect is given in Table 8.

Table 7

Moderation effect

	(O)	(M)	SD	T Statistics	P Values
Artificial Intelligence Learning ->Entrepreneurial Orientation	0.848	0.837	0.045	18.713	0
Attitude Towards Entrepreneurship ->Entrepreneurial Performance	0.119	0.127	0.041	2.929	0.004
Entrepreneurial Orientation ->Exploitation	0.671	0.671	0.069	9.678	0
Entrepreneurial Orientation ->Exploration	0.889	0.889	0.023	39.491	0
Exploitation ->Entrepreneurial Performance	0.752	0.732	0.069	10.928	0
Exploration ->Entrepreneurial Performance	0.156	0.168	0.066	2.343	0.019
Government Funding ->Entrepreneurial Orientation	0.127	0.139	0.048	2.629	0.009
Moderating Effect 1 ->Entrepreneurial Performance	0.042	0.042	0.015	2.816	0.006
Moderating Effect 2 ->Entrepreneurial Performance	0.022	0.025	0.049	0.452	0.651
Moderating Effect 3 ->Entrepreneurial Orientation	0.028	0.03	0.011	2.626	0.01

Fig. 4

Moderation Effect.

Table 8

Indirect Effect

	(O)	(M)	SD	T Statistics	P Values
Artificial intelligence learning ->Entrepreneurial orientation ->Exploitation ->Entrepreneurial performance	0.412	0.404	0.043	9.688	0
Entrepreneurial orientation ->Exploitation ->Entrepreneurial performance	0.492	0.487	0.049	10.139	0
Government funding ->Entrepreneurial orientation ->Exploitation ->Entrepreneurial performance	0.061	0.065	0.027	2.27	0.024
Artificial intelligence learning ->Entrepreneurial orientation ->Exploration ->Entrepreneurial performance	0.112	0.111	0.044	2.526	0.012
Entrepreneurial orientation ->Exploration ->Entrepreneurial performance	0.134	0.136	0.057	2.358	0.019
Government funding ->Entrepreneurial orientation ->Exploration ->Entrepreneurial performance	0.017	0.019	0.013	1.292	0.197
Artificial intelligence learning ->Entrepreneurial orientation ->Exploitation	0.562	0.555	0.048	11.714	0
Government funding ->Entrepreneurial orientation ->Exploitation	0.083	0.091	0.041	2.017	0.044
Artificial intelligence learning ->Entrepreneurial orientation ->Exploration	0.745	0.738	0.049	15.122	0
Government funding ->Entrepreneurial orientation ->Exploration	0.11	0.117	0.044	2.517	0.012

Figure 5 shows the moderation effect of government funding between artificial intelligence learning and entrepreneurial orientation. It is indicated that governmental funding strengthens the positive relationship between artificial intelligence learning and entrepreneurial orientation. Figure 6 shows the moderation effect of attitude towards entrepreneurship between exploration and entrepreneurial performance. It is highlighted that attitude towards entrepreneurship strengthen the positive relationship between exploration and entrepreneurial performance.

Fig. 5

Moderation effect of government funding between artifcial intelligence learning and enterprenurial orinetation.

Fig. 6

Moderation effect of attitude towards enterpenurship between exploration and enterprenurial performance.

Finally, Fig 6 shows the r-square value. R-square value for entrepreneurial performance is 0.875 which is substantial. It is highlighted that all the variables namely; artificial intelligence learning, entrepreneurial orientation, exploration, exploitation, government funding and attitude towards entrepreneurship are expected to bring 87.5% change in entrepreneurial performance. Additionally, predictive relevance was examined to achieve the certain quality of the model. It is shown in Table 9. Accor-ding to its value must be greater than zero.

Table 9

Predictive relevance (Q²)

	SSO	SSE	Q² (= 1-SSE/SSO)
Entrepreneurial orientation	1,302.00	510.569	0.608
Entrepreneurial performance	1,085.00	381.814	0.648
Exploitation	1,085.00	740.626	0.317
Exploration	868	396.21	0.544

5 Discussion and conclusion

The objective of the study was to examine the role of artificial intelligence learning to promote entre-preneurship performance with the help of entrepre-neurial orientation and strategic entrepreneurship. Moreover, the moderating role of government funding and attitude towards entrepreneurship was also examined. To achieve the objective of this study, a survey was carried out among the Malaysian universities. Partial Least Square (PLS)-Structural Equation Modeling (SEM) was employed to test the hypotheses.

This study introduced artificial intelligence learning at higher educational institutions and found that it has important role in entrepreneurship activities among the university students. It is found that artificial intelligence learning has central importance in entrepreneurship. It effects positively on the entrepreneurship by increasing the entrepreneurship orientation. Universities where the artificial intelligence learning is offering, students are more willing to learn about entrepreneurship activities. Therefore, it has important role to boost entrepreneurship activities at university level. Thus, Malaysian universities should introduce artificial intelligence learning opportunities at university level to expediate entrepreneurship activities among the students.

Additionally, the role of government is most crucial to expediate artificial intelligence learning at university level. Universities requires funding to start artificial intelligence learning activities, it this case, government can promote by providing the funding to the universities. As it is proved by the current study that government funding has positive role to enhance the relationship between artificial intelligence learning and entrepreneurship orientation. It is also proved by the literature that government funding has important role to expediate entrepreneurship activities [3 , and 43].

Along with the positive effect of artificial intelligence learning on entrepreneurship orientation, entrepreneurship orientation has positive effect on strategic entrepreneurship. This study considered two elements of strategic orientation, namely; exploration and exploitation. Entrepreneurship orientation lead to the strategic entrepreneurship which contribute towards entrepreneurship performance. Entrepreneurship orientation effect positively on both exploration and exploitation. Therefore, Malaysian universities should also promote exploration and exploitation. These results are in line with [49 57 and 58].

Moreover, it is found that exploration and exploitation have positive effect on entrepreneurship performance. Increase in the activities of exploration and exploitation, increase the entrepreneurship performance of university students. A study carried out by [14] found that the relationship of exploration and exploitation is significant positive with entrepreneurship. Both the elements of strategic entrepreneurship increase the entrepreneurship performance.

Finally, it is found that attitude towards entrepre-neurship is one of the key elements to get success in business. The current study found that attitude towards entrepreneurship has moderating role between strategic entrepreneurship and entrepreneurship performance. Attitude towards entrepreneurship strengthen the positive relationship between strategic entrepreneurship and entrepreneurship performance. [53] also found that entrepreneurship influenced by attitude towards entrepreneurship activities. Therefore, universities should develop various strategies to build the positive attitude of students towards entrepreneurship. Hence, it is investigated that artificial intelligence learning has positive role to enhance entrepreneurial orientation and entrepreneurial orientation promote strategic entrepreneurship with lead to the entrepreneurship performance among the university students. It is also revealed that government funding is important to enhance artificial intelligence learning.

5.1 Implications of the study

The current study has several implications for the universities to boost entrepreneurship activities. Particularly, this study introduced artificial intelligence learning among the higher educational institutions. This is the pioneer study which revealed that artificial intelligence learning is an important element of entrepreneurship which should be introduced among the universities to increase the student interest and capabilities in entrepreneurship development. Therefore, this study is beneficial for universities to promote entrepreneurship with the help of artificial intelligence learning. Additionally, this study provided a research framework for universities to boost entrepreneurship performance among the young entrepreneurs. This study is also beneficial for government, as this study proved that government funding is important to carry out artificial intelligence learning among universities.

Footnotes

Appendix

Table A1

Factor Loadings

	Artificial Intelligence Learning	Attitude Towards Entrepreneurship	Entrepreneurial Orientation	Entrepreneurial Performance	Exploitation	Exploration	Government Funding
AE1	0.894
AE3	0.917
AE4	0.908
AE5	0.889
AIL1		0.888
AIL2		0.887
AIL3		0.893
AIL4		0.876
AIL5		0.916
AIL6		0.878
AIL7		0.88
EO1			0.86
EO2			0.921
EO3			0.9
EO4			0.92
EO5			0.861
EO6			0.83
EP1				0.862
EP3				0.917
EP4				0.911
EP5				0.931
EP7				0.878
EXPLR1					0.902
EXPLR2					0.903
EXPLR3					0.867
EXPLR5					0.774
EXPOI1						0.905
EXPOI2						0.873
EXPOI3						0.892
EXPOI4						0.833
EXPOI5						0.893
GF1							0.885
GF2							0.825
GF3							0.905
GF4							0.919
GF5							0.842

Table A2

Cross-Loadings

	Artificial intelligence learning	Attitude towards entrepreneurship	Entrepreneurial orientation	Entrepreneurial performance	Exploitation	Exploration	Government funding
AE1	0.911	0.894	0.477	0.571	0.538	0.538	0.55
AE3	0.952	0.917	0.503	0.579	0.532	0.576	0.555
AE4	0.956	0.908	0.556	0.617	0.579	0.593	0.59
AE5	0.923	0.889	0.623	0.663	0.634	0.664	0.641
AIL1	0.888	0.933	0.831	0.608	0.583	0.8	0.604
AIL2	0.887	0.917	0.766	0.594	0.637	0.8	0.586
AIL3	0.893	0.921	0.821	0.583	0.581	0.798	0.587
AIL4	0.876	0.895	0.761	0.596	0.607	0.782	0.591
AIL5	0.916	0.954	0.849	0.683	0.668	0.834	0.678
AIL6	0.878	0.887	0.824	0.603	0.599	0.774	0.6
AIL7	0.88	0.892	0.87	0.644	0.615	0.843	0.624
EO1	0.799	0.477	0.86	0.549	0.531	0.748	0.555
EO2	0.839	0.567	0.921	0.657	0.626	0.811	0.637
EO3	0.831	0.488	0.9	0.627	0.583	0.796	0.629
EO4	0.833	0.57	0.92	0.673	0.625	0.817	0.649
EO5	0.786	0.536	0.861	0.65	0.621	0.801	0.613
EO6	0.794	0.55	0.83	0.626	0.565	0.734	0.616
EP1	0.557	0.629	0.548	0.862	0.798	0.685	0.78
EP3	0.669	0.623	0.683	0.917	0.855	0.748	0.873
EP4	0.634	0.607	0.665	0.911	0.857	0.764	0.836
EP5	0.662	0.635	0.678	0.931	0.863	0.77	0.872
EP7	0.597	0.549	0.64	0.878	0.783	0.678	0.854
EXPLR1	0.818	0.57	0.813	0.671	0.947	0.902	0.653
EXPLR2	0.843	0.58	0.832	0.676	0.913	0.903	0.686
EXPLR3	0.821	0.547	0.836	0.625	0.882	0.867	0.616
EXPLR5	0.638	0.583	0.578	0.835	0.873	0.774	0.811
EXPOI1	0.628	0.636	0.607	0.837	0.905	0.915	0.791
EXPOI2	0.604	0.537	0.588	0.825	0.873	0.926	0.799
EXPOI3	0.632	0.563	0.624	0.822	0.892	0.93	0.781
EXPOI4	0.578	0.525	0.529	0.761	0.833	0.87	0.744
EXPOI5	0.59	0.534	0.601	0.817	0.893	0.921	0.785
GF1	0.607	0.557	0.643	0.806	0.752	0.688	0.885
GF2	0.554	0.502	0.555	0.762	0.733	0.643	0.825
GF3	0.612	0.592	0.617	0.863	0.828	0.708	0.905
GF4	0.643	0.612	0.642	0.854	0.797	0.742	0.919
GF5	0.59	0.58	0.599	0.813	0.776	0.718	0.842

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Artificial intelligence learning and entrepreneurial performance among university students: evidence from malaysian higher educational institutions

Abstract

Keywords

1 Introduction

Table 1 Business Facilities 2013, Asia Pacific Position (Government Business Facilities (Position) Country 2011 2012 2013 Singapore 1 1 1 Hong Kong 2 2 2 New Zealand 3 3 3 Korea Selatan 15 8 8 Australia 11 15 10 Malaysia 23 18 12 Taiwan 24 25 16

4 Data analysis and results

5.1 Implications of the study

Footnotes

Appendix

References

Table 1
Business Facilities 2013, Asia Pacific Position (Government Business Facilities (Position)

Country 2011 2012 2013

Singapore 1 1 1

Hong Kong 2 2 2

New Zealand 3 3 3

Korea Selatan 15 8 8

Australia 11 15 10

Malaysia 23 18 12

Taiwan 24 25 16