The effect of perceived ease of use, benefits, and risks on intention in using the quick response code Indonesian standard

Ratnawati, Andalan Tri; Malik, Ahmad Article The effect of perceived ease of use, benefits, and risks on intention in using the quick response code Indonesian standard Global Business & Finance Review (GBFR) Provided in Cooperation with: People & Global Business Association (P&GBA), Seoul Suggested Citation: Ratnawati, Andalan Tri; Malik, Ahmad (2024) : The effect of perceived ease of use, benefits, and risks on intention in using the quick response code Indonesian standard, Global Business & Finance Review (GBFR), ISSN 2384-1648, People & Global Business Association (P&GBA), Seoul, Vol. 29, Iss. 7, pp. 110-125, https://doi.org/10.17549/gbfr.2024.29.7.110 This Version is available at: https://hdl.handle.net/10419/306031 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Introduction The use of technology is an important element that cannot be separated from human life. Increasingly advanced technology will shift the role of cash to non-cash forms in electronic payment systems that are more effective and efficient. This has an impact on the emergence of new innovations in electronic Received: Apr. 6, 2024; Revised: May. 27, 2024; Accepted: May. 31, 2024 † Corresponding author: Andalan Tri Ratnawati E-mail: [email protected] payment transactions via e-wallets or digital wallets. The existence of electronic payments with digital wallets will make it easier for the public to make payment transactions at any time (Chandra & Kohardinata, 2021). Digital wallets that exist today make payments via Quick Response Code (QR Code) more popular, especially in the business world. QR Code is a development of technology that is used as a payment technique in digital wallets. Various merchants are starting to provide digital payment services via QR Code. One standard QR is used, product and service providers (merchants) do not have to have a different GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 7 (AUGUST 2024), 110-125 pISSN 1088-6931 / eISSN 2384-1648∣Https://doi.org/10.17549/gbfr.2024.29.7.110 ⓒ 2024 People and Global Business Association GLOBAL BUSINESS & FINANCE REVIEW www.gbfrjournal.org for financial sustainability and people-centered global business1) The Effect of Perceived Ease of Use, Benefits, and Risks on Intentio n in Using the Quick Response Code Indonesian Standard A ndalan Tri Ratnawati†, Ahmad Malik F aculty of Economics and Business, Universitas 17 Agustus 1945 Semarang, Indonesia A B S T R A C T Purpose: This study aims to determine the effect of perceived ease of use, perceived benefits, and perceived risk of using QRIS among people in the city of Semarang. Design/methodology/approach: The population used is all users of the QRIS payment system, the sample size is 200 respondents, with the sampling technique using purposive sampling. The data source used is primary data, with data collection methods using questionnaire. The data analysis technique used is Partial Least Square. Findings: The results of hypothesis testing showed that perceived ease of use had a positive and significant effect on the intention to use QRIS, perceived benefits had a positive and significant effect on the intention to use QRIS, and perceived risk had a negative and significant effect on the intention to use QRIS. Research limitations/implications: The limitations of the study are the sample and the intention factor used. Future research needs to expand its scope and explore other factors such as the availability of IT infrastructure and the quality of service from vendors. Originality/value: The study contributes to TAM theory by focusing on the using of the e-money payment system by Quick Response Code Indonesian Standard (QRIS). Other contribution is test and compare the covariance based structural equation modeling (CB-SEM) software i.e. SmartPLS v. 4, AMOS v. 25, and Lisrel Lisrel v12.4.4. Keywords: Perceived ease of use, Perceived of benefit, Perceived of risk, Intention to use, CB-SEM ⓒ Copyright: The Author(s). This is an Open Access journal distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution , and reproduction in any medium, provided the original work is properly cited. Andalan Tri Ratnawati, Ahmad Malik 111 type of QR Code. Problems occur when more and more types of digital wallet applications appear, because merchants must provide several QR codes, according to the number of digital wallet applications available to scan. The type of QR code that is being increasingly provided by sellers or service providers (merchants) results in consumers having difficulty scanning QR codes, considering that each QR code provided by each application has different terms and conditions (Silaen et al., 2021). As the holder of the National Payment Gateway (GPN) regulation, this requires Bank Indonesia (BI) to have a system that can combine various instruments and payment channels nationally. Bank Indonesia has created a more integrated system by establishing a QR Code payment standard in facilitating digital payment transactions in Indonesia called the Quick Response Indonesia Standard (QRIS). QRIS is a QR code created by regulators together with the Indonesian Payment System Association (ASPI), with the aim of streamlining digital payment systems safely, supporting government productivity, and accelerating digital financial inclusion. QRIS offers a variety of benefits for both merchants and consumers, so that with the many advantages available, it will attract consumers to make QRIS a payment method when making purchases (Pontoh et al., 2022). The Technology Acceptance Model (TAM) is widely accepted as a theoretical framework that explains users' acceptance and use of technology (Davis, 1985, 1989). TAM has been applied in various contexts to explain behavior toward new digital products and service technologies (Musa et al., 2024). Perceived usefulness was found to be a more significant factor than ease of use in technology acceptance (Noor, 2024). In the field of digital payments, TAM is used to investigate the determining factors for the adoption of financial technology (Dianty & Faturohman, 2023), such as Fintect among small and medium enterprises (Krah et al., 2024). adoption of mobile payment technology (Natakusumah et al., 2023), crypto currency (Islam et al., 2023), acceptance of mobile money transaction services (Kelly & Palaniappan, 2023), blockchain (Kumari & Devi, 2023), and e-swallow (Tian et al., 2023). Several studies have explored the factors that influence consumers' interest in adopting QR Code payments. Research by Ozkaya et al. (2015) shows that purpose of use, practical use, awareness, perceived convenience, and current knowledge have a positive relationship with the level of QR Code use. This is reinforced by a study by Rotsios et al. (2022) which concluded that knowledge, understanding, and self-confidence influence consumer behavior using QR Codes. The study by Nandru et al. (2023) confirmed that performance expectations, effort expectations, facilitating conditions, personal innovation, and digital financial literacy are the determining factors for the use of QR Code payments in India. Likewise LiébanaCabanillas et al. (2015) concluded that attitude, innovativeness, and subjective norms have a positive relationship with the intention to use QR code payment technology in Spain. Tran et al. (2024) found that embedded traceability information resulted in consumers' willingness to pay using a QR Code for food products. Study by Imani and Anggono (2020) proves that habits, facilitating conditions, hedonic motivation, and performance expectancy significantly influence the intention to use QR Codes among the Z Generation in Bandung, Indonesia. Furthermore, (Kosim & Legowo, 2021) prove that business expectations, social influence, perceived trust, perceived risk, perceived regulatory support, promotional benefits, performance expectations, age, and effort expectations have a significant impact on the intention to use QR Codes among corporate customers in Jakarta, Indonesia. Intention to use as the level of how strong a person's desire or drive is to carry out certain behaviors (Kurniawan et al., 2022). Intention in using QRIS is the level of how strong a customer's desire is to use QRIS continuously both now and in the future because he feels compelled and performs a behavior to achieve certain goals. Someone's intention in information technology, especially in electronic payments arises because of more attention to the object, where attention will lead to a desire to know, learn, and prove further. There are several factors GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 7 (AUGUST 2024), 110-125 112 that can influence intention in using technology, including perceived ease of use, perceived benefits, and perceived risks (Chandra & Kohardinata, 2021; Reepu & Arora, 2022). Perceived ease of use is a situation where a person believes that using a certain system does not require effort or in other words the technology can be easily understood and used by users (Ismail, 2016). Ease of use of information technology can be one of the factors that a person pays attention to when utilizing payment information technology, because ease of use will allow users to continue to use it continuously. This means that if someone has confidence that the technology is easy to understand and use, they will be more interested in using the technology (Pontoh et al., 2022). The next factor that can affect the intention to use information technology is the perceived benefits. Perceived benefits as a belief in expediency, namely at the level of users (users) believe that using technology or a system will improve their performance at work. The increasing benefits of technology will affect someone who is interested in using it. This shows the high benefits that can be felt by someone when using technology, the higher a person's intention to use it (Chandra & Kohardinata, 2021). The third factor that can affect the intention to use is perceived risk, which is a thought about the risks that a person may experience due to uncertainty and other negative consequences that can be accepted for using a product or service (Reepu & Arora, 2022). Perceived risk has a strong role in reducing consumer interest in taking part in electronic transactions so that risk perception may have a negative influence on consumer interest in using information technology. This shows that the high risk of using technology from electronic payments will reduce consumer interest in using it (Cunrawasih & Fasyni, 2023). The study of the effect of perceived ease of use, perceived benefits, and perceived risk on the intention to use QRIS in the research will be carried out among people in the city of Semarang. Semarang City was chosen as the object of research for the reason that Semarang City is currently experiencing a fairly rapid development of information technology, especially regarding electronic payments via QRIS. It is hoped that people who are currently faced with developing payment technology will know more about payment methods through QRIS, besides that the choice of Semarang City is also expected to facilitate the research process considering the location of the researcher is also in Semarang City. Therefore, choosing the people of Semarang City will make it easier for researchers to get the number of samples that have been determined or needed in research, meaning that using samples will provide convenience in conducting research. Research on the effect of perceived ease of use, perceived benefits, and perceived risk on intention to use has also been carried out by several previous researchers. These studies found perceptions Ease of use has a significant positive effect on interest in using QR Code (Kurniawan et al., 2022; Pontoh et al., 2022). Another studies found that perceived ease of use has a positive but not significant effect on intention to use (Hapsoro & Kismiatun, 2022; Silaen et al., 2021). Research by Narahdita et al. (2020), Sari (2022), and Kurniawan et al. (2022) found that perceived benefits have a positive and significant effect on intention to use. Research by Silaen et al. (2021) found that perceived benefits have a positive but not significant effect on intention to use. Research conducted by Sari (2022), and Kurniawan et al. (2022) who found that perceived risk had a negative and significant effect on intention to use. Research by Pontoh et al. (2022) found that perceived risk has a negative but not significant effect on intention to use. Based on the inconsistency of previous results, the first contribution of this study is to reexamine TAM on the use of QR Codes as payment. The second contribution, this research contributes to the field of inferential statistics, through CB-SEM testing. Structural Equation Modeling (SEM), consisting of CB-SEM and PLS-SEM, is usually used to explain several statistical relationships simultaneously through visualization and model validation (Dash & Paul, 2021). Some scholars use the PLS-SEM approach Andalan Tri Ratnawati, Ahmad Malik 113 to TAM, even though they use the reflective measurement model (Al-Adwan et al., 2024; Mushi, 2024). Researchers chose PLS-SEM over CB-SEM because it was considered to have more advantages than high efficiency in parameter estimation (Wibowo et al., 2024). However, several scholars stated that this kind of composite or formative measurement model approach allows errors in the estimation of latent factor models (Afthanorhan et al., 2020; Aguirre-Urreta et al., 2024). In fact, for compositebased models, PLS-SEM is worth considering, while for factor-based models CB-SEM must be used (Dash & Paul, 2021). One of the CB-SEM software which being developed is SmartPLS 4.0.9 (Ringle et al., 2022; Sarstedt & Cheah, 2019). Although several studies have compared the use of CB-SEM with PLS-SEM with SmartPLS (Afthanorhan, 2013; El Maalmi et al., 2022; Nam et al., 2018; Purwanto et al., 2020; Rožman et al., 2020). However, none have compared CB-SEM Amos with CB-SEM SmartPLS. Therefore, in this study, SmartPLS calculation results were compared with the well-established CB-SEM i.e. IBM SPSS Amos version 26 (Arbuckle, 2019) and Lisrel version 12.4.4 (Jöreskog et al., 2016). Based on the descriptions, this research aims to analyze: first, the influence of perceived ease of use, benefits, and risks on the intention to use the Indonesian Standard Quick Response Code (a case study of the community in Semarang City). The second aim is to compare the two CB-SEM software based on various parameters and to provide solutions for research based on factor measurements. II. Literature Reviews A. Perceived Ease of Use on Intention Using QRIS Perceived ease of use indicates how far a person feels confident when using the information technology does not require great effort, and the technology can be easily understood and used and free from problems. Perceived ease of use in this case is connoted with the desire of the bank for various reasons to provide many conveniences in using QRIS as a digital payment instrument, such as being able to relate to mobile commerce technology and not requiring a large effort, as well as easy to understand and more efficient use. If someone has confidence in an information technology, feels that the technology is easy to use, then that person will use it. The results of the research by Ismail (2016) stated that the higher the user's perceived ease of use, the higher the intention to use smartphone. The results of Kurniawan et al. (2022) also show that with a high perceived ease of use a person will have a positive influence on the intention to use e-money. This is reinforced by the research results of (Huang, 2013), Wilson et al. (2021), Kurniawan et al. (2022), Pontoh et al. (2022), and Hikmah et al. (2023) which state that perceived ease of use has a positive effect on intention to use. Based on this description, the hypothesis proposed is: H1: perceived ease of use has a positive and significant effect on the intention to use QRIS B. Effect of Perceived Benefit on Intention to Use QRIS Perceived benefit is a measure of the extent to which a person feels confident and believes that a system or information technology will provide benefits and advantages in improving performance, productivity and effectiveness at work. Perceived benefits in this case are connoted with the bank's desire for various reasons to provide many advantages in using QRIS as a digital payment method, such as more effective and efficient transactions, errors in transactions can be minimized, and there are no difficulties in conversion. Therefore, the higher the benefits that can be felt by someone when using technology, the higher the intention to use it. The results of the research by Kurniawan et al. (2022) stated that with a higher perception of benefits GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 7 (AUGUST 2024), 110-125 114 from a person, it will have a positive impact on increasing the intention to use e-money. The results of research conducted by Sari (2022) state that the higher the perceived benefits shown by someone, the higher the intention to use e-business. This result is reinforced by the research results of Rachbini (2018), Narahdita et al. (2020), and Chandra and Kohardinata (2021) which stated that perceived benefit has a significant positive effect on intention to use. Based on this description, the hypothesis proposed is: H2: perceived benefits have a positive and significant effect on the intention to use QRIS. C. Effect of Perceived Risk on Intentions to Use QRIS Perceived risk shows a person's views or thoughts on the negative potential caused by uncertainty or unwanted consequences when carrying out online activities or transactions. Perceived risk has a strong role in reducing consumer interest in taking part in electronic transactions so that it is possible that risk perception will have a negative effect on consumer intentions in using information technology products. Perceived risk arises as a form of uncertainty in certain circumstances that a person will consider to make a decision between yes and no. Therefore, the higher the level of perceived risk in using QRIS, the lower the intention to use it. The results of the research by Reepu and Arora (2022) which state that the higher the perceived risk that is felt by someone, will have a negative impact on the intention to use online banking. The results of research by Sari (2022) also state that with a higher risk perception in using online transactions, it will reduce the customer's intention to use. This is reinforced by the results of research by Song and Liu (2021), Silaen et al. (2021), Yang et al. (2021) Kurniawan et al. (2022), and Cunrawasih and Fasyni (2023) which state that perceptions of risk significant negative effect on intention to use. Based on this description, the hypothesis proposed is: H3: perceived risk has a negative and significant effect on the intention to use QRIS. Figure 1 illustrates the relationship between variables that model development was based on the theory of Technology Acceptance Model (TAM). III. Method A. Samples The population used in this study is people who make payments via QRIS in the city of Semarang. Considering that the population size is not known with certainty, then the determination of the number of samples that will use G*Power 3 statistical analysis (Faul et al., 2007), obtained a sample of 200 respondents. The sampling technique used in this study is purposive sampling, which is a sampling technique based on chance, that is, anyone who live in Semarang city and familiar with QRIS. B. Measurement Perceived ease of use is a feeling of confidence and trust that arises from a person regarding the extent to which using information technology does not require great effort, and the technology can be easily understood and used and free from problems. H1 + H2 + H3 - Perceived Ease of Use (PE) Perceived Benefit (PB) Perceived Risk (PR) Intention to Use QRIS (IU) Figure 1. Research conceptual framework Andalan Tri Ratnawati, Ahmad Malik 115 The variable indicators consist of: technology that is easy to obtain, technology that is easy to learn, technology that is easy to understand, technology that is easy to operate (Pontoh et. al, 2022; Kurniawan et. al, 2022). Perceived benefit is a measure of the extent to which a person feels confident and believes that a system or information technology or an available application will provide benefits and advantages in improving performance, productivity and effectiveness at work. This variable is measured by: speeding up transactions, increasing efficiency during transactions, increasing effectiveness during transactions, providing a sense of security when making transactions (Sari, 2022; Kurniawan et. al, 2022). Perception of risk is defined as a person's view or thought of the negative potential caused by uncertainty or unwanted consequences that can become a barrier for someone to carry out activities or transactions online. The indicators are: the presence of certain risks, disturbances that cause losses, the level of security that is not guaranteed, thoughts about risk (Kurniawan et.al, 2022; Pontoh et. al , 2022). The intention to use QRIS is the level of how strong a customer's desire is to use QRIS continuously both now and in the future because he feels compelled and performs a behavior to achieve certain goals. The indicators include: interest in using, interest in long-term use, transactional interest, interest based on preferential, intensity of use (Kurniawan et.al, 2022; Pontoh et.al , 2022). Methods of data collection using a questionnaire with Likert five scale, with the terms 1 indicating strongly disagree, 2 disagree, 3 neutral, 4 agree, and 5 indicating strongly agree. C. Non-Response Bias Testing This non-response bias test was conducted by comparing respondents who returned the questionnaire before the return deadline with those who did not return the questionnaire on time. Respondents who answered were represented by questionnaires that arrived earlier (within the specified time limit), while those who did not answer were by questionnaires that arrived in the last period (after the deadline for returning the questionnaire). The t-test showed insignificant results (p>0.05), indicating no significant difference between the two groups. Therefore it can be stated that the research data is free from this bias. D. Data Analysis This study uses the SPSS software package was used to analyze descriptive and inferential statistic. Inferential data analysis used Covariance Based Structural Equation Model (CB-SEM) with the SmartPLS version 4.0.9, than compare with IBM SPSS Amos and Lisrel. The validity test can be seen from the loading factor value for each construct indicator. The criteria for passing the validity test is the loading factor value, if the value is between 0.6-0.7. Discriminant validity test will also be carried out by looking at the Fornell-Larcker Criterion, Cross Loadings, and Heterotrait-Monotrait Ratio (HTMT). Furthermore, to measure the reliability of a construct with a reflective indicator can be done by looking at the value of Cronbach's alpha, composite reliability, and Average Variant Extracted (AVE). The basis used for assessing construct reliability is the value of the Cronbach's alpha and composite reliability must be greater than 0.7, and the AVE value must be greater than 0.5. Hypothesis testing in this study was used to determine the partial effect of the variable's perceived ease of use. perceived benefits. and perceived risk on the intention to use QRIS. Hypothesis testing is done by comparing the calculated t value with the t table. if the t-calculated value is > 1.96 and the p-value is <0.05. then the result is to accept the alternative hypothesis. Finally, an Independent-Sample t-test was carried out to compare the average values produced by SmartPLS with Amos/Lisrel as CB-SEM Analysis. GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 7 (AUGUST 2024), 110-125 116 IV. Results This survey research was conducted from May 2023 to April 2024 by distributing 220 questionnaires to the response targets. Field data collection is carried out with online assisted by Google Forms. Furthermore, as many as 200 answers were used for research. This number is stated to be sufficient in accordance with the CB-SEM (Hair et al., 2010). A. Respondent Descriptive This study uses frequency and percentage statistics to describe demographic profiles and the results are summarized in Table 1. Based on the table, more respondents were female with value of 124 (62%) than male value of 76 (46%), aged majority between 26-35 years with value 120 (60%). Based on this data, information can be obtained that the majority of respondents are the millennial generation. B. Measurement Result Test the validity of the convergent indicators carried out using SmartPLS, Amos, and Lisrel. Table 2 shows that from the outer loading results the value of each variable indicator is greater than 0.70. From these results it can be concluded that each indicator used to measure perceived ease of use, perceived benefits, perceived risks, and intention to use QRIS can be said to be valid. This means that each indicator is able to measure the research variable. The discriminant validity test can be seen in the Table 3. The calculation results show that the root value of AVE on the diagonal is higher than the correlation between the construct and other constructs below it. Other results show that the HTMT value in above the diagonal does not exceed 0.9. This finding indicates that the evaluation of the validity of the indicators used is fulfilled and that the indicators are truly unique in measuring their construct, not other constructs. Furthermore, the results in Table 4 show Cronbach's alpha and composite reliability values for each variable: perceived ease of use, perceived benefits, perceived risk, and intention to use QRIS are greater than 0.70, and the Average Variant Extracted (AVE) value for perceived ease of use, perceived benefits, perceived risks, and intention to use QRIS is greater than 0.50. These results can be concluded that each research variable's construct can be reliable and has fulfilled the requirements for research. C. Structural Model Analysis The second stage of PLS-SEM testing is testing the structural model which consists of the path coefficient for hypothesis testing. The structural Demographic Characteristics Frequency Percentage Gender Man 76 38% Woman 124 62% Age Under 21 years 6 3% 21 years to 25 years 30 15% 26 years to 30 years 38 19% 31 years to 35 years 82 41% 36 years to 40 years 20 10% 41 years to 45 years 14 7% 46 years or older 10 5% Table 1. Respondent's profile Andalan Tri Ratnawati, Ahmad Malik 117 model (inner model) produced in this study can be seen in the Figure 2 for Graphically of SmartPLS, Figure 3 for Amos, and Figure 4 for Lisrel. Its shows that perceived ease of use and perceived benefit have a positive effect on the intention to use QRIS, while a negative influence is found on perceived risk. Table 5 shows that the results of the analysis show that the R-Square value are 0.313 of SmartPLS and 0.352 for Amos/Lisrel. These results can be interpreted that perceived ease of use, perceived benefits. and perceived risk can explain the variation in the intention to use the QRIS with moderate category(Chin, 1998). The variation in the intention to use variable is explained by other variables not examined or not included in models, such as trust, level of understanding, service features, subjective norms, security, promotion, and others. The goodness of fit test uses 10 criteria as shown in Table 6. The overall fit index shows that for both chi-square and p-value criteria, all software does not Construct Code Outer Loading SmartPLS AMOS Lisrel Perceived Ease of Use PE1 PE2 PE3 PE4 0.776 0.809 0.795 0.831 0.836 0.897 0.849 0.831 0.836 0.897 0.849 0.831 Perceived Benefit PB1 PB2 PB3 PB4 PB5 0.908 0.881 0.899 0.876 0.905 0.872 0.849 0.878 0.835 0.897 0.872 0.849 0.878 0.835 0.897 Perceived Risk PR1 PR2 PR3 PR4 0.891 0.909 0.902 0.865 0.685 0.729 0.738 0.754 0.685 0.729 0.738 0.754 Intention to Use QRIS IU1 IU2 IU3 IU4 IU5 0.784 0.834 0.858 0.863 0.852 0.695 0.777 0.825 0.842 0.829 0.695 0.777 0.825 0.842 0.829 Table 2. V alidity testing Variables PE PB PR IU Perceived Ease of Use (PE) 0.787 0.740 0.389 0.723 Perceived Benefit (PB) 0.604 0.771 0.598 0.795 Perceived Risk (PR) -0.325 -0.499 0.785 0.724 Intention to Use QRIS (IU) 0.602 0.679 -0.606 0.791 Table 3. Fornell-Larcker criterion and HTMT discriminant v alidit y Construct Cronbach's Alpha Composite Reliability Average Variance Extracted (AVE) Perceived Ease of Use 0.795 0.867 0.620 Perceived Benefit 0.829 0.879 0.594 Perceived Risk 0.793 0.865 0.616 Intention to Use QRIS 0.849 0.892 0.625 Table 4. Reliability testing GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 7 (AUGUST 2024), 110-125 124 References Afthanorhan, A. (2013). A comparison of partial least square structural equation modeling (PLS-SEM) and covariance based structural equation modeling (CB-SEM) for confirmatory factor analysis. International Journal of Engineering Science and Innovative Technology, 2(5), 198-205. Afthanorhan, A., Awang, Z., & Aimran, N. (2020). Five common mistakes for using partial least squares path modeling (PLS-PM) in management research. Contemporary Management Research, 16(4), 255-278. Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M. (2024). 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