Mediating role of fundamental anomalies on the relationship between behavioral factors and investment performance
Vol 9, Issue 1, 2024, Article identifier:
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Abstract
This study examines the behavioral factors like heuristics, prospect factors, emotions, and social interaction on the investment performance of the listed firms in Saudi Arabia. Furthermore, the association between behavioral factors and investment performance is investigated through the mediating role of fundamental anomalies. For data collection, the questionnaire technique was applied by utilizing the items from existing literature linked with the variables of interest. Furthermore, data were empirically examined through descriptive statistics, demographic analysis, and a two-step (measurement model and structural model) approach using the Statistical Package for the Social Sciences (SPSS) and Smart (partial least squares) PLS. Under the measurement model, the study items’ reliability, validity, and internal consistency were investigated. The study findings through the measurement model confirm the reliability and validity of the latent constructs as measured through selected items. On the other side, the structural model affirms a significant and positive impact of emotions, heuristics, and social interaction on investment performance in the Saudi Stock Exchange. Moreover, fundamental anomalies significantly mediate the relationships between heuristics factors and investment performance, emotions and investment performance, and social interactions and investment performance. Conclusively, the empirical findings would greatly support various stakeholders, including existing and proposed investors, financial analysts, stockbrokers, and governmental policymakers interested in judging the role of behavioral factors and market anomalies toward investment performance in Saudi Arabia.
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1. Rossi M, Gunardi A. Efficient market hypothesis and stock market anomalies: Empirical evidence in four European countries. Journal of Applied Business Research 2018; 34(1): 183–192. doi: 10.19030/jabr.v34i1.10111
2. Sánchez-Granero MA, Balladares KA, Ramos-Requena JP, Trinidad-Segovia JE. Testing the efficient market hypothesis in Latin American stock markets. Physica A: Statistical Mechanics and Its Applications 2020; 540: 123082. doi: 10.1016/j.physa.2019.123082
3. Kapoor S, Prosad JM. Behavioural finance: A review. Procedia Computer Science 2017; 122: 50–54. doi: 10.1016/j.procs.2017.11.340
4. Ogunlusi OE, Obademi O. The impact of behavioural finance on investment decision-making: A study of selected investment banks in Nigeria. Global Business Review 2019; 22(9): 0972150919851388. doi: 10.1177/0972150919851388
5. Forbes W. Behavioural Finance. John Wiley & Sons; 2009.
6. Tversky A, Kahneman D. Availability: A heuristic for judging frequency and probability. Cognitive psychology 1973; 5(2): 207–232.
7. Kahneman D, Tversky A. Prospect theory: an analysis of decisions and the psychology of choice. Econometrica 1979; 47: 313–327.
8. Waweru NM, et al. The effects of behavioural factors in investment decision-making: a survey of institutional investors operating at the Nairobi Stock Exchange. International Journal of Business Emerging Markets 2008; 1(1): 24–41.
9. De Bondt WF, Thaler R. Does the stock market overreact? The Journal of finance 1985; 40 (3): 793-805.
10. Montier J, Strategy GE. Applied behavioural finance: Insights into irrational minds and market. 2002; Unpublished work.
11. Eom C, Park JW. Effects of the fat-tail distribution on the relationship between prospect theory value and expected return. The North American Journal of Economics Finance 2020; 51: 101052.
12. Fischer P, Greitemeyer T. Music and aggression: The impact of sexual-aggressive song lyrics on aggression-related thoughts, emotions, and behavior toward the same and the opposite sex. Personality Social Psychology Bulletin 2006; 32(9): 1165–1176.
13. Poduska JM, Kellam SG, Wang W, et al. Impact of the good behavior game, a universal classroom-based behavior intervention, on young adult service use for problems with emotions, behavior, or drugs or alcohol. Drug Alcohol Dependence 2008; 95: S29–S44.
14. Andrade EB, Ariely D. The enduring impact of transient emotions on decision making. Organizational Behavior and Human Decision Processes 2009; 109(1): 1–8.
15. Cheng PYK. Decision utility and anticipated discrete emotions: An investment decision model. Journal of Behavioral Finance 2014; 15(2): 99–108.
16. Abdin SZ, Farooq O, Sultana N, Farooq M. The impact of heuristics on investment decision and performance: Exploring multiple mediation mechanisms. Research in International Business Finance 2017; 42: 674–688.
17. Schwert GW. Anomalies and market efficiency. In: Handbook of the Economics of Finance. Elsevier; 2003; pp. 939–974.
18. Fama EF. The behavior of stock-market prices. The Journal of Business 1965; 38(1): 34-105.
19. Economics T. Saudi Arabia Stock Market (TASI). Available online: https://tradingeconomics.com/saudi-arabia/stock-market (accessed on 2 August 2023).
20. Alsabban S, Alarfaj O. An empirical analysis of behavioral finance in the Saudi Stock Market: Evidence of overconfidence behavior. International Journal of Economics Financial Issues 2020; 10(1): 73–86.
21. Akhtar F, Das N. Investor personality and investment performance: From the perspective of psychological traits. Qualitative Research in Financial Markets 2020; 12(3): 333–352.
22. Kengatharan L, Kengatharan N. The influence of behavioral factors in making investment decisions and performance: Study on investors of Colombo Stock Exchange, Sri Lanka. Asian Journal of Finance & Accounting 2014; 6(1): 1.
23. Keswani S, Dhingra V, Wadhwa B. Impact of behavioral factors in making investment decisions and performance: Study on investors of National Stock Exchange. International Journal of Economics and Finance 2019; 11(8): 80.
24. Khawaja MJ, Alharbi ZN. Factors influencing investor behavior: an empirical study of Saudi Stock Market. International Journal of Social Economics 2021; 48(4): 587–601.
25. Firat D, Fettahoglu S. Investors’ purchasing behaviour via a behavioural finance approach. International Journal of Business Management 2011; 6(7): 153–163.
26. Gregoriou A, Healy JV, Le H. Prospect theory and stock returns: A seven factor pricing model. Journal of Business Research 2019; 101: 315–322.
27. Barberis N, Mukherjee A, Wang B. Prospect theory and stock returns: An empirical test. The Review of Financial Studies 2016; 29(11): 3068–3107.
28. Figueiredo PS, Loiola E. The impact of project introduction heuristics on research and development performance. RAI Revista de Administração e Inovação 2017; 14(2): 151–161.
29. Gino F, Pisano G. Do Manager’s Heuristics Affect R & D Performance Volatility? A Simulation Informed by the Pharmaceutical Industry. Harvard Business School; 2006.
30. Johnson J, Tellis GJ. Blowing bubbles: Heuristics and biases in the run-up of stock prices. Journal of the Academy of Marketing Science 2005; 33(4): 486–503.
31. Zeferino FRM. Investor Heuristics Measurement and Return Predictability—A Behavioural Finance Study [Master’s thesis]. Nova School of Business and Economics; 2021.
32. Mahmood Z, Kouser R, Abbas SS, Saba I. The effect of hueristics, prospect and herding factors on investment performance. Pakistan Journal of Social Sciences 2016; 36(1): 475–484.
33. Alsedrah IT. Influence of heuristics and framing factors on speculating and investment behavior: Evidence from individual traders in the Saudi Stock Market/Ibrahim Tawfeeq Alsedrah. In: The Doctoral Research Abstracts. IGS Biannual Publication; 2016.
34. Olatunji SO, Al-Ahmadi MS, Elshafei M, Fallatah YA. Saudi Arabia stock prices forecasting using artificial neural networks. In: Proceedings of the Fourth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2011); 4–6 August 2011; Stevens Point, WI, USA. pp. 81–86.
35. Kumaran S. Impact of personal epistemology, heuristics and personal attributes on investment decisions. International Journal of Financial Research 2013; 4(3): 62.
36. Lee BS, Li L. The idiosyncratic risk-return relation: A quantile regression approach based on the prospect theory. Journal of Behavioral Finance 2016; 17(2): 124–143.
37. Ohk S, Ju BG. Capitalizing on prospect theory value: The Asian developed stock markets. Japan the World Economy 2021; 57: 101042.
38. Wang J, Wu C, Zhong X. Prospect theory and stock returns: Evidence from foreign share markets. Pacific-Basin Finance Journal 2021; 69: 101644.
39. Li L, Hwang NCR. Prospect Theory and Earnings Manipulation: Examination of the Non-Uniform Relationship between Earnings Manipulation and Stock Returns Using Quantile Regression. Available online: http://130.217.226.42/wai/econwp/1725_Li_Hwang.pdf (accessed on 2 August 2023).
40. Matoussi H, Mostafa MA. An investigation of Investors Bias on Saudi stock exchange. International Research Journal of Finance and Economics 2016; 31: 66-81.
41. Alrubaishi D, Alarifi G. Does family involvement matter in initial public offering underpricing? Evidence from Saudi Arabia. International Journal of Management, Economics Social Sciences 2019; 8(3): 157–168.
42. Shen J, Griffith J, Najand M, Sun L. Predicting stock and bond market returns with emotions: Evidence from futures markets. Journal of Behavioral Finance 2021; 24(3): 333–344.
43. Hasan MSB, Kumar A, Taffler R. Investor emotions and the cross-section of stock returns. Available online: https://www.efmaefm.org/0EFMAMEETINGS/EFMA%20ANNUAL%20MEETINGS/2021-Leeds/papers/EFMA%202020_stage-1301_question-Full%20Paper_id-417.pdf (accessed on 2 August 2023).Duxbury D, Gärling T, Gamble A, Klass V. How emotions influence behavior in financial markets: A conceptual analysis and emotion-based account of buy-sell preferences. The European Journal of Finance 2020; 26(14): 1417–1438.
44. Duxbury D, et al. How emotions influence behavior in financial markets: a conceptual analysis and emotion-based account of buy-sell preferences. The European Journal of Finance 2020; 26(14): 1417–1438.
45. Iqbal S, Bilal AR. Investment performance: Emotional beasts are dragging into the darkness of the castle. Global Business Review 2021; 2021: 09721509211044309.
46. Pornpikul C, Nettayanun S. Stock return drivers: A mix of reasons and emotions. Review of Behavioral Finance 2021; 14(5): 751–771.
47. Chun J, Ahn J, Kim Y, Lee S. Using deep learning to develop a stock price prediction model based on individual investor emotions. Journal of Behavioral Finance 2020; 22(4): 480–489.
48. Majewski S, Tarczynski W, Tarczynska-Luniewska M. Measuring investors’ emotions using econometric models of trading volume of stock exchange indexes. Investment Management and Financial Innovations 2020; 17(3): 281–291.
49. VanderPal GA. Emotional quotient and intelligence quotient on behavioral finance and investment performance. Journal of Marketing Development Competitiveness 2021; 15(2).
50. Kaplanski G, Levy H. Investment performance and emotions: An international study. Studies in Economics Finance 2019; 36(1): 32–50.
51. Mariappan S, Rani S. Empirical study on business and emotions (market) cycles and sector performance in Indian Stock Market. In: Proceedings of the International Conference on Evidence Based Management 2015; (ICEBM 2015); 20–21 March 2015; Pilani, Rajasthan, India.
52. Willman P. Thinking, feeling and deciding: The influence of emotions on the decision making and performance of traders. Strategic Direction 2012; 28(4).
53. Fenton-O’Creevy M, Soane E, Nicholson N, Willman P. Thinking, feeling and deciding: The influence of emotions on the decision making and performance of traders. Journal of Organizational Behavior 2011; 32(8): 1044–1061.
54. Ady SU, Tyas AM, Farida I, Gunawan AW. Immediate and expected emotions toward stock returns through overconfidence and cognitive dissonance, the study of Indonesian investor behavior. PalArch’s Journal of Archaeology of Egypt/Egyptology 2020; 17(3): 1140–1165.
55. Cabrera-Paniagua D, Cubillos C, Vicari R, Urra E. Decision-making system for stock exchange market using artificial emotions. Expert Systems with Applications 2015; 42(20): 7070–7083.
56. Zhou Z, Zhao J, Xu K. Can online emotions predict the stock market in China? In: Cellary W, Mokbel M, Wang J (editors). Web Information Systems Engineering—WISE 2016, Proceedings of the 17th International Conference on Web Information Systems Engineering (WISE 2016); 7–10 November 2016; Shanghai, China.
57. Cordes H, Nolte S, Schneider JC. Dynamics of stock market developments, financial behavior, and emotions. Journal of Banking & Finance 2023; 154: 106711.
58. Almanie T, Aldayel A, Alkanhal G, et al. Saudi Mood: A real-time informative tool for visualizing emotions in Saudi Arabia Using Twitter. In: 2018 21st Saudi Computer Society National Computer Conference (NCC); 25–26 April 2018; Riyadh, Saudi Arabia. pp. 1–6.
59. Alamro R, McCarren A, Al-Rasheed A. Predicting saudi stock market index by incorporating GDELT using multivariate time series modelling. International Conference on Computing, Springer; 2019, pp. 317–328.
60. Akhtar F, Thyagaraj KS, Das N. The impact of social influence on the relationship between personality traits and perceived investment performance of individual investors: Evidence from Indian stock market. International Journal of Managerial Finance 2017.
61. Liang P, Guo S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics 2015; 43(4): 883–901.
62. Hvide H, Östberg P. Social interaction at work: Co-worker influence on stock investments. Available online: https://www.nccr-finrisk.uzh.ch/media/pdf/SFIResearchDay2013_Oestberg_paper.pdf (accessed on 2 August 2023).
63. Haddad M, Hakim S. Irrational exuberance on the Saudi Stock Exchange. In: Proceedings of the ERF’s 15th Annual Conference; 23–25 November 2008; Cairo, Egypot.
64. Wasiuzzaman S. Seasonality in the Saudi Stock Market: The Hajj effect. The Quarterly Review of Economics Finance 2018; 67: 273–281.
65. Khan K, Nasir MA, Rossi M. The calendar anomalies on performance and volatility of stock market: The effects of Ramadan on Karachi Stock Exchange. Global Business Economics Review 2017; 19(1): 54–69.
66. Keim DB. Size-related anomalies and stock return seasonality: Further empirical evidence. Journal of Financial Economics 1983; 12(1): 13–32.
67. Gultekin MN, Gultekin NB. Stock return anomalies and the tests of the APT. The Journal of Finance 1987; 42(5): 1213–1224.
68. Hoffman A. Stock return anomalies: Evidence from the Johannesburg Stock Exchange. Investment Analysts Journal 2012; 41(75): 21–41.
69. Sehgal S, Subramaniam S, De La Morandiere LP. A search for rational sources of stock return anomalies: Evidence from India. International Journal of Economics Finance 2012; 4(4): 121–134.
70. Tangjitprom N. The calendar anomalies of stock return in Thailand. Journal of Modern Accounting Auditing 2011; 7(6): 565.
71. Jang J. Stock return anomalies and individual investors in the Korean stock market. Pacific-Basin Finance Journal 2017; 46: 141–157.
72. Öztürk MB, Uysal M, Arslan H, et al. The impact of calendar anomalies on stock return and volatility: evidence from Turkish stock market. Ömer Halisdemir Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi 2018; 11(1): 221-238.
73. Anchev S. Investor base and stock return anomalies. Available online: http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1237641&dswid=-2524(accessed on 2 August 2023).
74. Chen T. Stock return anomalies from ending-digit effects around the world. Global Economic Review 2017; 46(4): 464–494.
75. Chen Y, Huang W, Jiang GJ. Do short‐term institutions exploit stock return anomalies? Financial Review 2022; 57: 69–94.
76. Robins RP, Smith GP. Selection bias and pseudo discoveries on the constancy of stock return anomalies. Review of Quantitative Finance and Accounting 2020; 55(4): 1407-1426. doi: 10.1007/s11156-020-00878-w.
77. Dong X, Li Y, Rapach D, Zhou G. Anomalies and the expected market return. The Journal of Finance 2022; 77(1): 639–681.
78. Wasiuzzaman S. Religious anomalies in Islamic stock markets: The Hajj effect in Saudi Arabia. Journal of Asset Management 2017; 18(3): 157–162.
79. Anjum S. Impact of market anomalies on stock exchange: A comparative study of KSE and PSX. Future Business Journal 2020; 6(1): 1–11.
80. Bingham CB, Eisenhardt KM. Rational heuristics: The ‘simple rules’ that strategists learn from process experience. Strategic Management Journal 2011; 32(13): 1437–1464.
81. Tversky A, Kahneman D. Judgment under uncertainty: Heuristics and biases. Science 1974; 185(4157): 1124–1131.
82. Gigerenzer G, Gaissmaier W. Heuristic decision making. Annual Review of Psychology 2011; 62: 451–482.
83. Shah AK, Oppenheimer DM. Heuristics made easy: An effort-reduction framework. Psychological Bulletin 2008; 134(2): 207–222.
84. Albar FM, Jetter AJ. Heuristics in decision making. In: PICMET'09-2009 Portland International Conference On Management Of Engineering & Technology; 2-6 August 2009; Portland, OR, USA. pp. 578–584.
85. Doswell III CA. Weather forecasting by humans—Heuristics and decision making. Weather and Forecasting 2004; 19(6): 1115–1126.
86. Lau RR, Redlawsk DP. Advantages and disadvantages of cognitive heuristics in political decision making. American Journal of Political Science 2001; 45(4): 951–971.
87. Busenitz LW, Barney JB. Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making. Journal of Business Venturing 1997; 12(1): 9–30.
88. Pompian MM, Wood AS. Behavioral Finance and Wealth Management: How to Build Optimal Portfolios for Private Clients. John Wiley and Sons, Inc; 2006.
89. Ahmad M, Shah SZA. Overconfidence heuristic-driven bias in investment decision-making and performance: Mediating effects of risk perception and moderating effects of financial literacy. Journal of Economic and Administrative Sciences 2020; 38(1): 60–90.
90. Chernoff E, Sriraman B. Probabilistic thinking. AMC 2014; 10: 12.
91. Simon M, Houghton SM, Aquino K. Cognitive biases, risk perception, and venture formation: How individuals decide to start companies. Journal of Business Venturing 2000; 15(2): 113–134.
92. Barber BM, Odean T. The behavior of individual investors. Handbook of the Economics of Finance 2013; 2(Part B): 1533-1570.
93. Moore DA, Healy PJ. The trouble with overconfidence. Psychological Review 2008; 115(2): 502–517.
94. Odean T. Do investors trade too much. American Economic Review 1999; 89(5): 1279-1298. doi: 10.1257/aer.89.5.1279
95. Tversky A, Kahneman D. Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk Uncertainty 1992; 5(4): 297–323.
96. Barberis N, Jin LJ, Wang B. Prospect theory and stock market anomalies. In: Proceedings of the 10th Miami Behavioral Finance Conference; 13 December 2019; Coral Gables, FL, USA.
97. Chien CC, Chen TC. Can the January anomaly in Taiwan’s stock market be explained by the prospect theory? Quantitative Finance 2008; 8(4): 335–339.
98. He XD, Zhou XY. Portfolio choice under cumulative prospect theory: An analytical treatment. Management Science 2011; 57(2): 315–331.
99. Bhootra A, Hur J. High idiosyncratic volatility and low returns: A prospect theory explanation. Financial Management 2014; 44(2): 295–322.
100. Zafar Z, Siddiqui DA. Behavioral finance perspectives on Pakistan stock market efficiency: Assessing the prospect theory empirically with an adaptive pattern of efficiency across military and democratic regimes. International Journal of Organizational Leadership 2020; 9(1): 20–38.
101. Barberis N, Huang M, Santos T. Prospect theory and asset prices. The Quarterly Journal of Economics 2001; 116(1): 1–53.
102. Zhong X, Wang J. Prospect theory and corporate bond returns: An empirical study. Journal of Empirical Finance 2018; 47: 25–48.
103. Hirshleifer D, Shumway T. Good day sunshine: Stock returns and the weather. The Journal of Finance 2003; 58(3): 1009–1032.
104. Loewenstein, G.F. et al. (2001) Risk as feelings. chological bulletin 127 (2), 267.
105. van Dolen WM, de Ruyter K, Streukens S. The effect of humor in electronic service encounters. Journal of Economic Psychology 2008; 29(2): 160–179.
106. Shiller RJ. Human behavior and the efficiency of the financial system. Handbook of Macroeconomics 1999; 1: 1305-1340.
107. Nofsinger JR. The Psychology of Investing. Routledge; 2017.
108. Olsen RA. Behavioral finance and its implications for stock-price volatility. Financial Analysts Journal 1998; 54(2): 10-18. doi: 10.2469/faj.v54.n2.2161
109. De Long JB, Shleifer A, Summers LH, Waldmann R. Positive feedback investment strategies and destabilizing rational speculation. The Journal of Finance 1990; 45(2): 379–395.
110. Edmans A, Garcia D, Norli Ø. Sports sentiment and stock returns. The Journal of Finance 2007; 62(4): 1967–1998.
111. Brock WA, Durlauf SN. Multinomial choice with social interactions. National Bureau of Economic Research 2003. doi: 10.3386/t0288
112. Hong H, Kubik JD, Stein JC. Social interaction and stock‐market participation. The Journal of Finance 2004; 59(1): 137–163.
113. Copur Z. Handbook of research on behavioral finance and investment strategies: Decision making in the financial industry: Decision Making in the Financial Industry. IGI Global; 2015.
114. Latif M, Arshad S, Fatima M, et al. Market efficiency, market anomalies, causes, evidences, and some behavioral aspects of market anomalies. Research Journal of Finance and Accounting 2011; 2(9): 1-13.
115. del Águila N. Behavioral finance: Learning from market anomalies and psychological factors. Revista de Instituciones, Ideas y Mercados 2009; 50: 47–104.
116. Yalçın KC. Market rationality: Efficient market hypothesis versus market anomalies. European Journal of Economic Political Studies 2010; 3(2): 23–38.
117. Chan KF, Gray P, Gray S, Zhong A. Political uncertainty, market anomalies and presidential honeymoons. Journal of Banking & Finance 2020; 113: 105749.
118. Patton AJ, Weller BM. What you see is not what you get: The costs of trading market anomalies. Journal of Financial Economics 2020; 137(2): 515–549.
119. Salman M, Khan B, Khan SZ, et al. The impact of heuristic availability bias on investment decision-making: Moderated mediation model. Business Strategy & Development 2021; 4(3): 246-257. doi: 10.1002/bsd2.148
120. Ahmad M, Shah SZA. Overconfidence heuristic-driven bias in investment decision-making and performance: Mediating effects of risk perception and moderating effects of financial literacy. Journal of Economic and Administrative Sciences 2020; 38(1): 60–90.
121. Mahmood Z, Kouser R, Abbas SS, Saba I. The effect of hueristics, prospect and herding factors on investment performance. Pakistan Journal of Social Sciences 2016; 36(1): 475–484.
122. Kesebir P, Gasiorowska A, Goldman R, et al. Emotional Style Questionnaire: A multidimensional measure of healthy emotionality. Psychological Assessment 2019; 31(10): 1234-1246. doi: 10.1037/pas0000745
123. Yang M, Mamun AA, Mohiuddin M, et al. Predicting stock market investment intention and behavior among Malaysian working adults using partial least squares structural equation modeling. Mathematics 2021; 9(8): 873. doi: 10.3390/math9080873
124. Phuoc Luong L, Thi Thu Ha D. Behavioral factors influencing individual investors decision-making and performance.: A survey at the Ho Chi Minh Stock Exchange. Asian Journal of Finance & Accounting 2011; 6(1).
125. Waweru NM, Munyoki E, Uliana E. The effects of behavioural factors in investment decision-making: a survey of institutional investors operating at the Nairobi Stock Exchange. International Journal of Business and Emerging Markets 2008; 1(1): 24-41. doi: 10.1504/IJBEM.2008.019243
126. Krejcie RV, Morgan DW. Determining sample size for research activities. Educational Psychological Measurement 1970; 30(3): 607–610.
127. Yao Y, Wang P, Jiang Y, et al. Innovative online learning strategies for the successful construction of student self-awareness during the COVID-19 pandemic: Merging TAM with TPB. Journal of Innovation & Knowledge 2022; 7(4): 100252. doi: 10.1016/j.jik.2022.100252
128. Jiang Y, Liu H, Yao Y, Li Q, Li Y. The positive effects of growth mindset on students’ intention toward self-regulated learning during the COVID-19 pandemic: A PLS-SEM approach. Sustainability 2023; 15(3): 2180. doi: 10.3390/su15032180
129. Li Q, Zeng J, Zhao B, et al. Nurses' preparedness, opinions, barriers, and facilitators in responding to intimate partner violence: A mixed-methods study. Journal of Nursing Scholarship 2023. doi: 10.1111/jnu.12929
130. Purwanto A. Education research quantitative analysis for little respondents: comparing of Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS. Jurnal Studi Guru Dan Pembelajaran 2021; 4(2).
131. Rouf MA, Akhtaruddin M. Factors affecting the voluntary disclosure: A study by using Smart PLS-SEM approach. International Journal of Law Management 2018; 60(6): 1498–1508
132. Thaker HMT, Sakaran KC, Nanairan NM, et al. Drivers of loyalty among non-Muslims towards Islamic banking in Malaysia: Evidence from SmartPLS. International Journal of Islamic Middle Eastern Finance Management 2020; 13(2): 281–302.
133. Kalkbrenner MT. Alpha, omega, and H internal consistency reliability estimates: Reviewing these options and when to use them. Counseling Outcome Research Evaluation 2021; 14(1): 77–88.
134. Le HTD, Bui MT, Nguyen GTC. Factors affecting electronic tax compliance of small and medium enterprises in Vietnam. The Journal of Asian Finance, Economics Business 2021; 8(1): 823–832.
135. Tomaro QPV, Genon LJ, Amod AF. Singled out: Invisibilization of single mothers in social welfare and protection policies. Jurnal Studi Pemerintahan 2021; 12(2): 132–155.
136. Abd Rahman NAAH, Ridhauddin M, Shahri INM, Hamid H. The study of green human resource management practices within the private and public sector. Asian Journal of Research in Business Management 2022; 4(2): 170–178.
137. Netemeyer RG, Bearden WO, Sharma S. Scaling procedures: Issues and applications. SAGE Publications; 2003.
138. Kumar R, Singh H. The Relationship Between Performance Measures and Overall Performance in the Manufacturing Environment. In: Khosrow-Pour DBA (editors). Encyclopedia of Organizational Knowledge, Administration, and Technology. IGI Global; 2021, pp. 341-354.
139. dos Santos PM, Cirillo MÂ. Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions. Communications in Statistics-Simulation Computation 2021; 52(4): 1639–1650.
140. Pervan M, Curak M, Pavic Kramaric T. The influence of industry characteristics and dynamic capabilities on firms’ profitability. International Journal of Financial Studies 2017; 6(1): 4.
141. Hilkenmeier F, Bohndick C, Bohndick T, et al. Assessing distinctiveness in multidimensional instruments without access to raw data-a manifest Fornell-Larcker criterion. Frontiers in Psychology 2020; 11: 223. doi: 10.3389/fpsyg.2020.00223
142. Ab Hamid M, Sami W, Sidek MM. Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series 2017; 890(1): 012163. doi: 10.1088/1742-6596/890/1/012163
143. Afthanorhan A, Ghazali PL, Rashid N. Discriminant validity: A comparison of CBSEM and consistent PLS using Fornell & Larcker and HTMT approaches. Journal of Physics: Conference Series 2021; 1874(1): 012085. doi: 10.1088/1742-6596/1874/1/012085
144. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 2014; 43(1): 115–135.
145. Hair JF, Sarstedt M, Hopkins L, Kuppelwieser VG. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review 2014; 26(2): 106–121.
146. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory Practice 2011; 19(2): 139–152
DOI: https://doi.org/10.54517/esp.v9i1.1847
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