Impact of the Use of Emerging Technologies on Organisational Knowledge-creation Capability by Task Complexity

Abstract

In the context of knowledge-based economy, an organisation’s ability to create knowledge is the most important factor in maintaining its competitiveness. Collective intelligence is posited as a paramount methodology not only for the generation of knowledge pertaining to multifaceted issues but also as a fundamental pillar within the framework of organizational knowledge management. The use of emerging technologies is an important strategy for improving the organisation’s ability to create knowledge through collective intelligence by adding depth and breadth of knowledge. However, excessive use of technology often has a negative impact on organisational knowledge management. Therefore, this study aims to identify the two-sided effect of using emerging technologies (big data analytics (BDA) and online platforms) on organisational knowledge creation according to the complexity of the task. The results of our study suggest that the use of BDA technology for organisational knowledge creation should be maintained at an appropriate level in general, but it is recommended to increase the use of BDA technology for low-complexity tasks. In addition, using online platform technology is difficult to consider as a strategic way to solve high-complexity tasks, but increasing the use of BDA technology can contribute to improving the organisation’s ability to create knowledge.

Keywords

Organisational knowledge creation collective intelligence groupthink bigdata analytics online platform task complexity

Introduction

Knowledge has emerged as a fundamental capability enabling contemporary organizations to endure and adapt to the profound transformations in their operating environments (Kim et al., 2014; Krishna, 2019). Since 2016, the fourth industrial revolution has created a big wave, changing not only industries but also our lifestyles (Kim et al., 2016; Lee et al., 2018). The fourth industrial revolution era is an era of knowledge through hyperconnectivity, decentralisation, sharing and openness (Schwab, 2017). These social and technological changes have brought three challenges for organisations, creating new knowledge faster and more effectively than before. First, the issues that need to be solved are becoming more complex. Most of them include multidisciplinary issues; therefore, they request the cooperation of diverse knowledge domains. The second challenge refers to the amount of knowledge needed for creating new knowledge. The speed of knowledge accumulation has increased dramatically with the advancement of information and communications technology (ICT). Consequently, the exploding amount of knowledge has prevented individuals from dealing with them. Lastly, modern society is increasingly demanding a higher level of creativity. Creative idea or knowledge is an important source of innovation and competitiveness because it is an inherent ability of human beings (Jung et al., 2023; Kim & Kim, 2018; Patra & Krishna, 2015; Yun et al., 2021).

The development of information technology has increased the effect of knowledge on not only innovative capability (Cassia et al., 2020; Kim & Shin, 2021; Kim et al., 2022; Na et al., 2023) but also collective intelligence (Täuscher, 2017). Since ICT has widened the scope of communication, people have been exposed to learning through knowledge transfer, sharing and recombination (Kim et al., 2016, 2021; Krishna, 2024; Robertson et al., 1996). Among ICTs, big data analytics (BDA) and online platform technology are the representative technologies that have intervened in the knowledge creation process of organisations in recent years. The combination of BDA and online platform technology helps an organisation solve problems using collective intelligence at a lower cost than before.

BDA has been known to support human decision-making and knowledge creation (Malone & Bernstein, 2015). Especially, BDA has advantages in some tasks that are beyond human capabilities, such as group classification, coexistence of characterisation, diversification (De Vincenzo et al., 2018) and digital archive (Bieber et al., 2002). The use of BDA can improve the level of organisational knowledge management capabilities, including efficiency, effectiveness, competitiveness and creativity (Kohn & Hüsig, 2006). Online platform technology is closely related to collective intelligence. The appropriate sharing platform of an organisation can improve not only organisational performance (Reia et al., 2019) but also collective intelligence through reinforcing social connectivity (Woolley et al., 2010).

However, excessive use of collective behaviours may be a source of inferior decision-making and organisational performance (Riccobono et al., 2016). When an organisation is in the process of decision-making or knowledge creation, a certain level of human capability is required, for example human insight, vision and organisational culture (Mcafee & Brynjolfsson, 2012). Additionally, BDA technology should involve investigations by humans (Kornienko et al., 2015), such as the verification of data consistency and the adequacy of sources (Janssen et al., 2017; Kadadi et al., 2014). Therefore, a balance between the use of technologies and human intervention should be maintained.

The type of task is an important aspect when applying new technologies (Engel et al., 2014). High-complex tasks usually require a higher level of cooperation. Since the high complexity of the task can increase the uncertainty of organisational outcomes (Turner & Pratkanis, 1998), it is more effective to use technology that helps widen and deepen knowledge for creating solutions (Argote, 1982).

Therefore, this study, an extension of the PhD dissertation (Cha, 2020), aims to identify the two-sided effect of emerging technologies (BDA and online platforms) on organisational knowledge creation according to the complexity of the task. For empirical analysis, we coduncted an online survey for 300 respondents and excluded some respondents who is inconsistent or untrustworthy. Finally, a survey dataset of 243 knowledge workers in Korea is used. This article is structured as follows. The second section provides a theoretical overview and hypothesis development. The third section describes the data and methods used in this study. Finally, the results and conclusions are set out in the fourth and fifth sections.

Theoretical Background and Modelling

Collective Intelligence and Organisational Knowledge Creation

Organisational knowledge is defined as complex individual knowledge (Cha et al., 2019, 2020; Davenport & Prusak, 1998). However, organisational knowledge is distinguished from a bundle of knowledge based on its synergetic convergence. Therefore, organisational knowledge needs to be managed by a specific process involving the members, culture and technology (Kim & Kim, 2000).

Recently, collective intelligence has emerged as a critical factor in the organisation’s knowledge management (Ahn & Lee, 20l09) in two aspects. First, when collective intelligence exists in a group, the quality of a group decision is superior to that of an individual one. Previous studies found, as evidence, that the intellectual ability of group members is likely to be irrelevant to the level of collective intelligence (Woolley et al., 2015). Second, collective intelligence is likely to expand the scope of the capabilities of a group. Interdisciplinary knowledge is easily drawn when collective intelligence operates well. Therefore, decentralisation and diversity are important factors for collective intelligence.

The use of technology is relevant to collective intelligence. Musser and O’reilly (2007) argued that enhancing participation through the accessibility of the database and network can lead to collective intelligence because the integration of dispersed knowledge has been adopted as one of the essential factors of organisational knowledge creation (Spielman, 2014) and collective intelligence (Lopez Flores et al., 2015a, 2015b).

Technology and Organisational Knowledge Creation

BDA and Organisational Knowledge Creation

BDA has been spotlighted as a powerful method to excavate hidden knowledge that humans cannot recognise. Therefore, the BDA system does not request any proposed answers for knowledge creation (Kvasnička & Pospíchal, 2015). Recently, BDA technology has progressed in many ways through artificial intelligence algorithms, including large language models (LLMs). Therefore, people expect artificial intelligence to deal with intellectual problems that are too complex and most delicate for humans to solve. BDA is likely to affect organisational efficiency, effectiveness, competitiveness and creativity (Kohn & Hüsig, 2006) with its novel properties. Besides, from the knowledge management perspective, BDA helps in sharing and transforming individual knowledge and reincarnating organisations into knowledge organisations (Liebowitz, 2001).

Despite its benefits, the introduction of BDA may not be an effective solution if certain conditions are not satisfied. BDA cannot be utilised in dealing with abstract concepts such as insight, vision or culture (Mcafee & Brynjolfsson, 2012). Also, BDA capability is required for good firm performance (Gupta & George, 2016; Shan et al., 2019). Fundamentally, the reliability of BDA should be examined by a human because its resources can be inconsistent and inadequate (Janssen et al., 2017; Kadadi et al., 2014). BDA is, therefore, an effective method for supporting the knowledge creation process with necessary human intervention.

H₁: The relationship between the use of BDA and oorganisational knowledge creation capability follows an inverted U-shape.

Online Platforms and Organisational Knowledge Creation

Collaborations on online platforms unprecedentedly enhanced not only organisational knowledge capacity but also individual capabilities (Sproull & Arriaga, 2007), and now the online platform is perceived as a critical channel for utilising collective intelligence (Alag, 2008; Musser & O’reilly, 2007). First, the online platform has a horizontal structure emphasising sharing and interaction (Engel et al., 2014; Woolley et al., 2010). Second, the properties of the online platform guarantee a high level of diversity (Spielman, 2014). Diversity of organisation is an essential factor in inducing collective intelligence (Loasby, 2002) because it prevents polarisation (Faraj et al., 2011) and enhances adaptability (Macal & North, 2005). Third, the online platform enables people’s interaction, the essence of collective intelligence (Massari et al., 2019).

However, previous studies have suggested the potential side effects of online platforms. Breitsohl et al. (2015) and Størseth (2018) argued that groupthink on an online platform could be more intensified than in an offline organisation. According to Størseth (2018), the reason for groupthink comes from compliance developed by exaggerated social sensitivity called ‘cyber conformity’. Breitsohl et al. (2015) revisited the Janis (1972) groupthink model to discover the significant factors of the online groupthink phenomenon and argued that group insulation and stress could increase the tendency of groupthink on financial online platforms. Besides, excessive use of online platforms can induce side effects such as moral hazard (Massari et al., 2019) and dogmatic behaviour by overindulgence (Faraj et al., 2011). Additionally, Dhir et al. (2018) argued that participating in an online platform can lead to ‘media fatigue’ which deteriorates both the physical and mental capabilities of humans.

H₂: The relationship between the use of an online platform and organisational knowledge creation capability follows an inverted U-shape.

The Effect of Task Complexity on Organisational Knowledge Creation

Knowledge collaboration in organisations is dependent on the type of task (Engel et al., 2014); therefore, it is an important aspect of determining organisational solutions. Complex tasks require a higher level of cooperation since connectivity and interdependence have been considered essential elements to make sense of the project (Harri, 2020). As the high complexity of the task can increase the uncertainty of organisational outcomes (Turner & Pratkanis, 1998), it is effective to use technologies for creating solutions (Argote, 1982).

Prior studies established that the high complexity of tasks increases the influence of collective intelligence on organisational knowledge creation. Similar to McHugh’s study in 2016, Langfred & Shanley (1997) argued that the complex interdependency of tasks can determine the effect of social cohesiveness on organisational knowledge creation. Also, the members of an organisation need to know the process of cooperation when the task and environment are complex (Chiocchio, 2007; Chiocchio et al., 2011). Finally, studies imply that the method of creating new knowledge depends on the complexity of the task.

H_3a: The high complexity of tasks intensifies the effect of the use of BDA on the organisational knowledge creation capability.

H_3b: The high complexity of tasks intensifies the effect of the use of online platforms on the organisational knowledge creation capability.

Data and Variables

Data

This study used a survey dataset of 254 knowledge workers in South Korea. This study conducted an online survey targeting knowledge workers in South Korea for five days from 1 November to 5 November 2019, through Macromil Embrain, an online survey specialist company. This survey sample is divided into the high task complexity group (n_high = 117) and the low task complexity group (n_low = 126) based on the average value of task complexity in the sample (average task complexity = 2.15, std. dev. = 1.13). Respondents were evenly distributed according to age, ranging from twenty to fifty years. The sample contained 51% female and 49% male respondents.

Variables

Dependent Variables

This study measures the organisational capability of knowledge creation through two conventional concepts: the level of collective intelligence and the level of groupthink. The level of collective intelligence was measured by the method used in Apperly (2012) and Woolley et al. (2010), and the level of groupthink also complies with the measurement of previous studies (Chapman, 2006; Hart, 1991). ‘Theory of Mind’ (ToM; Apperly, 2012) was highlighted as a potential measurement of collective intelligence because of its correlation with the capability of organisational knowledge creation (Engel et al., 2014). Therefore, this study combined the concepts of ToM (Apperly, 2012) and social sensitivity (Woolley et al., 2010) to measure the level of collective intelligence. This study measured the level of groupthink through both the symptoms proposed by Leana (1985) and the basic concepts by Janis (1982): overestimation, closed-mindedness and uniformity pressure. All items were measured on a five-point Likert scale. The capability of organisational knowledge creation (CAP_i) is calculated by the ratio between the level of collective intelligence and that of groupthink.

C A P_{i} = \frac{Score of collective {intelligence}_{i}}{Score of {groupthink}_{i}}

(1)

Independent Variables

The use of technologies was measured by the combination of several items. This study follows the measurement of perceived usefulness and ease of use in the technology acceptance model (Davis et al., 1989). Perceived usefulness, perceived ease of use, perceived risk, trust and priority are also considered for measuring the use of technologies (Pavlou, 2003). First, perceived usefulness is defined as a level of belief that the use of technology increases individual performance (Davis, 1989). As technology covers both BDA and online platforms, it is rational to adopt it as a measurement of technology usage. Second, the perceived ease of use refers to the level of belief that technology can be utilised without additional effort (Davis, 1989). The domain of ease of use covers the actual usage to get some of the requisite abilities. This study adopted the items of Davis (1989) and modified them to suit the context of BDA and online platform technologies. Third, perceived risk is defined as an uncertainty of technology usage (Cha & Lee, 2019); in other words, it refers to the differences between the expectation and the actual result of technology usage (Sweeney et al., 1999). We used the questions of Im et al. (2008) to measure the perceived risk of technology usage. Trust in technology can be an important determinant of its use (Pavlou, 2003). Trust is defined as an expectation that another party behaves based on the expectation that it will perform particular actions (Allen & Wilson, 2003). Unlike traditional offline trust, online trust is created by interactions among people (Bart et al., 2005). As trust has been defined, examined and operationalised in many ways (McCloskey, 2011), this study borrowed the concept of trust to fulfil the users’ expectations (Warkentin et al., 2002). Priority of technology usage is the same concept as the intention to reuse in the technology acceptance model (Davis, 1989).

Control Variables

This study adopted four control variables, which have been considered significant sources of organisational knowledge creation. Organisational diversity is measured by two aspects: gender and background knowledge (Woolley et al., 2010, 2015). Organisational equality involves three types of equality: opportunity, importance and atmosphere (Woolley et al., 2010). Individual capability is measured by the level of task-related ability and background knowledge (Bates & Gupta, 2017). The organisation size is defined as the number of employees (Jang & Park, 2015; Kim & Shin, 2021; Kim et al., 2021a, 2021b).

In this study, the complexity of the task is used as a segmentation criterion for stage 2 analysis. The complexity of a task is defined as an average score of three aspects: interdependency, multidisciplinary and time-consuming (Casey-Campbell & Martens, 2009; Harri, 2020; Malone & Bernstein, 2015). In this study, the interdependence of tasks was measured by the item developed by Van der Vegt et al. (2001). The multidisciplinary property was defined as a structural relationship among different specialised groups and time-consuming was defined as the average duration of a task (Ben-menahem & Schneider, 2016). Summary statistics for variables are in Table 1.

Table 1.

Description and Summary Statistics of Variables.

Variable	Measures				Mean	Standard Deviation	Skew	References
Dependent variable	Groupthink symptom	Overestimation		GT1	3.384	0.8	−0.38	Chapman (2006) Hart (1991)
		Overestimation		GT2	3.164	0.88	−0.32	Chapman (2006) Hart (1991)
		Closed-mindedness		GT3	2.696	0.93	0.33	Janis (1982)
		Closed-mindedness		GT4	3.116	1.06	0.1
		Uniformity pressure		GT5	3.152	0.91	0.19
		Uniformity pressure		GT6	2.576	0.84	−0.45
	Collective intelligence	ToM	Empathic ability	CI1	2.844	0.99	0.16	Apperly (2012)
		ToM	Emotional sensitivity	CI2	2.624	0.92	0.4	Apperly (2012)
		Social sensitivity	Sensitivity to other’s reaction	CI3	3.148	0.96	0.31	Woolley et al. (2010)
		Social sensitivity	Concern to other’s reaction	CI4	3.252	0.8	−0.5	Woolley et al. (2010)
Explanatory variable	The use of big data analytics	Perceived usefulness		BDA1	2.552	0.83	−0.14	Davis (1989)
		Perceived ease of use		BDA2	2.964	0.84	−0.21	Davis (1989)
		Perceived risk		BDA3	2.752	0.85	0.33	Im et al. (2008)
		Trust		BDA4	2.632	0.81	0.44	Gefen (2000) Warkentin et al. (2002)
		Priority		BDA5	2.636	0.85	0.3	Gefen (2000) Warkentin et al. (2002)
	The use of online platform	Perceived usefulness		OP1	2.828	0.86	0.36	Davis (1989)
		Perceived ease of use		OP2	2.632	0.85	0.18	Davis (1989)
		Perceived risk		OP3	2.844	0.85	0.06	Im et al. (2008)
		Trust		OP4	2.816	0.86	0.34	Warkentin et al. (2002)
		Priority		OP5	2.732	0.87	0.36	Warkentin et al. (2002)
Control variable	Organisational diversity	Gender		OD1	2.955	1.51	0.33	Woolley et al. (2010)
	Organisational diversity	Background knowledge		OD2	3.355	0.86	−0.53	Woolley et al. (2010)
	Organisational equality	Opportunity		OE1	3.325	0.90	−0.43	Woolley et al. (2010)
		Importance		OE2	3.298	0.90	−0.39
		Atmosphere		OE3	3.096	0.91	−0.16
	Individual capability	Level of background knowledge		IC1	2.744	0.87	0.09	Woolley et al. (2010), Bates and Bates and Gupta (2017)
		Task relativeness		IC2	3.116	0.83	0.34
		Task understanding		IC3	3.088	0.72	0.09
	Organisation size	Number of employees		OS1	3.084	0.94	0.034	Kim et al. (2021)
Segmentation criteria	Task complexity	Interdependency		TC1	3.292	0.74	−0.26	Van der Vegt et al. (2001)
		Multidisciplinary		TC2	3.092	0.68	−0.21	Ben-menahem and Schneider (2016)
		Time-consuming		TC3	1.996	0.71	0	Ben-menahem and Schneider (2016)

Regression Model

In this study, two econometric models were used for analysis. For stage 1 analysis (H₁ and H₂), both linear and polynomial regression models were used to provide a clear and direct description of the coefficients of the two models (Yang et al., 2008). Each empirical model is applied into three categories based on the degree to which the technology is dominantly used in the organisation. BDA (online platform) refers to the case that the use of the BDA (online platform) is a dominant technological capability, and the integrated model represents the case that both technologies are used equally. As mentioned above, to clarify the single effect of Use_OP and Use_BDA, we compared six regression models classified by two criteria: independent variable and high-order term, as shown in Table 2.

Table 2.

Six Types of Regression Model.

Independent Variable	Linear Model	Polynomial Model
Use of online platform	Use_OP	Use_OP, Use_OP,²
Use of big data analytics	Use_BDA	Use_BDA, Use_BDA²
Both	Use_BDA, Use_OP	Use_BDA, Use_BDA², Use_OP, Use_OP²

For stage 2 analysis (H₃), we applied a polynomial regression model. To analyse the difference in the results according to the task complexity, we divide the samples into three types: high complexity, low complexity and integrated model.

Results

Table 3 identifies the regression model better suited to capture the effect of technology usage on organisational knowledge-creation capability. The use of BDA (Use_BDA) significantly affects knowledge-creation capability in the linear regression model and in the polynomial regression model. In the polynomial model, both the first-order term (coeff = 0.32777, p = 5.37e−7) and the quadratic term (coeff = −0.09412, p = .00884) of the use of BDA are statistically significant. The minus sign of the quadratic term refers to the fact that the relationship between BDA usage and the capability of organisational knowledge creation is concave. These results are similarly shown in the unified regression model, too. These results support H₁.

Table 3.

Results of Impact of Use of Technologies on Organisational Knowledge-creation Capability (Stage 1 Analysis).

Variables	BDA		Online Platform		Integrated (BDA+OP)
Variables	Model 1 (Linear)	Model 2 (Polynomial)	Model 1 (Linear)	Model 2 (Polynomial)	Model 1 (Linear)	Model 2 (Polynomial)
Use_BDA	2.92E−01**(6.29E−02)	0.3278**(0.0636)	–	–	3.08E−01**(6.30E−02)	0.3373***(0.0639)
Use_BDA ²	–	-0.0941** (0.036)	–	–	–	−0.0838*(0.0365)
Use_OP	–	–	8.44E−02(6.28E−02)	0.075(0.0631)	1.21E−01*(6.05E−02)	0.1070(0.0604)
Use_OP ²	–	–	–	0.0581(0.0434)	–	0.0318(0.0422)
Org_div	3.95E−016***(6.22E−02)	0.3914***(0.0614)	5.21E−01***(6.03E−02)	0.5206***(0.0602)	4.08E−01***(6.21E−02)	0.4041***(0.0615)
Org_equal	−3.37E−02(6.26E−02)	-0.0305(0.0618)	−1.06E−01(6.69E−02)	−0.1153(0.0672)	−6.78E−02(6.45E−02)	−0.0676(0.0643)
Ind_cap	−3.39E−02(6.16E−02)	-0.0453(0.0610)	−4.78E−02(6.42E−02)	−0.0444(0.0641)	−4.22E−02(6.14E−02)	−0.0499(0.0610)
Org_size	6.14E−02(6.17E−02)	0.0619(0.0610)	1.42E−01*(6.26E−02)	0.1387*(0.0625)	7.15E−02(6.16E−02)	0.0695(0.0610)
R ²	0.4373	0.4505	0.3892	0.3892	0.4439	0.4592
Prob>F	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Constant	−2.08E−16(4.81E−02)	−0.09374(0.05935)	9.19E−17(5.00E−02)	-0.0579(0.0661)	−1.47E−16(4.78E−02)	−0.1151*(0.0682)
Observations	243	242	243	243	242	240

Notes: *p < .1; **p < .05; ***p < .01.

Reported figures are the coefficients of the variables.

Standard errors are indicated in parentheses.

BDA: Big data analytics; OP: Online platform.

The use of online platforms (Use_op) is not a significant factor in both regression models. Both first-order and second-order terms of the use of online platforms are found to be insignificant even in the polynomial regression model. The use of online platforms significantly influences the capability of organisational knowledge creation only in the unified regression model. Therefore, online platform usage does not enhance knowledge-creation capabilities. Thus, H₂ is to be rejected.

Table 4 shows the comparison result of the research model by task complexity. ‘High complexity’ refers to the result of a group that recognises that their task is relatively complex. Accordingly, the inverse U relationship between the use of BDA and organisational knowledge-creation capability is maintained regardless of its task complexity. However, a different result is derived in the low-complexity task group. The ‘low complexity’ in Table 4 shows that the use of both BDA and online platforms has positive effects on the capability of organisational knowledge creation. Unlike the case of high-complexity tasks, the use of both technologies has linear relationships when the complexity of the task is relatively lower. Therefore, the decrease in effectiveness due to the amount of technology usage does not occur. These results can be evidence supporting H_3a and rejecting H_3b.

Table 4.

Results of Impact of Use of Technologies on Organisational Knowledge-creation Capability According to Task Complexity (Stage 2 Analysis).

Variable	High Complexity	Low Complexity	Integrated (High + Low)
Use_BDA	0.3320** (0.1004)	0.2678** (0.0827)	0.3373*** (0.0639)
Use_BDA ²	−0.1402* (0.0702)	−0.0430 (0.0423)	−0.0838* (0.0365)
Use_OP	0.0670 (0.0901)	0.1823* (0.0825)	0.1070 (0.0604)
Use_OP ²	−0.0237 (0.0637)	0.0735 (0.0594)	0.0318 (0.0422)
Org_div	0.2055* (0.1010)	0.5745*** (0.0796)	0.4041*** (0.0615)
Org_equal	−0.1321 (0.0986)	−0.0200 (0.0822)	−0.0676 (0.0643)
Ind_cap	−0.0207 (0.0941)	−0.0405 (0.0795)	−0.0499 (0.0610)
Org_size	0.1196 (0.09342)	0.05616 (0.0787)	0.0695 (0.0610)
R ²	0.3364	0.3892	0.4592
Prob>F	0.0000	0.0000	0.0000
Constant	−0.0695 (0.0995)	−0.1750* (0.0902)	−0.1151* (0.0682)
Observations	115	116	240

Notes: *p < .1; **p < .05; ***p < .01.

Reported figures are the coefficients of the variables.

Standard errors are indicated in parentheses.

Conclusion

Discussion

Although the use of technology can help improve the performance and efficiency of an organisation in general (Alavi & Leidner, 2001; Faraj et al., 2011; Täuscher, 2017), it is likely to have the opposite effects if it is used excessively or incorrectly (Davenport & Bean, 2019; Saide & Sheng, 2020). Therefore, in this study, it was assumed that the use of technology and the creation of organisational knowledge would have an inverted U-shaped relationship and that high task complexity can further strengthen this relationship (Chiocchio, 2007; Langfred & Shanley, 1997; McHugh et al., 2016).

Results showed that the use of BDA and the organisational knowledge-creation capability satisfied the inverted U-shaped relationship, but the use of online platforms did not have a significant relationship. Therefore, supported by results, BDA technology is an effective way to support human decision-making and knowledge creation (Malone & Bernstein, 2015) when an organisation uses it at the appropriate level. However, online platforms do not impact the capability of organisational knowledge creation, contrary to the results of previous studies supporting their positive effect on organisational performance (Ma & Agarwal, 2007; Tapscott & Williams, 2006).

This study proposes two possible explanations. First, a lack of reliability can lead to a loss of responsibility for the accuracy of information or knowledge (Faraj et al., 2011; Rains, 2007). Online platforms usually do not include reliability factors such as transparency (Prahalad & Ramaswamy, 2004) or peer evaluations (Flanagin & Metzger, 2008). Therefore, online platforms do not affect positively the organisational knowledge-creation capability.

Second, the bounded rationality of people narrowed the scope of cognition; thus, people actually communicate with smaller groups than the whole size of the network (Koohborfardhaghighi et al., 2017). Consequently, when the volume of knowledge exceeds the capability of an organisation, it becomes difficult to utilise online platforms as a source of knowledge (Lane et al., 2006). Although organisations can acquire this ability through the accumulation of prior knowledge (Lichtenthaler & Lichtenthaler, 2009), it involves considerable costs (Gebregiorgis & Altmann, 2015; Haile & Altmann, 2016).

The second analysis aims to identify the indirect effects of task complexity. The result shows that if the task complexity is relatively low, both the use of the BDA and the online platform monotonously positively impact the capability of organisational knowledge creation. Therefore, the more technology is used in low-complexity situations, the better the knowledge-creation capability, regardless of the intensity of use. This can be explained in terms of a sociotechnological perspective. A complex task requires a high level of interaction between technology and humans (Pidgeon & O’Leary, 2000; Turner & Pidgeon, 1998).

Previous studies highlighted two aspects inducing inefficiency in the use of BDA in high-complexity tasks. The first aspect is related to the prior decision-making process of the organisation. When the task is highly complex, the uncertainty of a solution increases (McCauley, 1998) so it requires high collective intelligence involving interactions among people. Despite the analysis being conducted by computers or machines, details such as input, algorithm to be used or result interpretation belong to humans. Previous literature emphasises that knowledge collaborations using various knowledge backgrounds are required to complete very complex tasks (Chiocchio, 2007; Hansen & Vaagen, 2016; Kim et al., 2023). Conversely, if the complexity of tasks is decreased, the importance of humans’ roles is also diminished. Therefore, the use of technology monotonously increases the capability of organisational knowledge creation in low-complexity task situations.

These findings conflict with the field study result of McHugh et al. (2016), which found that high task complexity reduces the importance of collective intelligence. In other words, the properties of technology and the tasks should be fitted by an appropriate method and qualified people (Langfred & Shanley, 1997). However, if the task is not complex (e.g., low interdependencies), the result of this study supports the simulation result of McHugh et al. (2016). The importance of discussion or interaction has decreased, but the effectiveness of formalised processes such as BDA and online platforms has increased (Argote, 1982; Malone & Bernstein, 2015).

The results of this study suggest that determining an appropriate level of use is also important in the utilisation of emerging technologies (Devaraj & Kohli, 2003). Proper use of technologies can help organisations create valuable knowledge for acquiring competitive advantage (Dewan, 1997; Hitt, 1995). This is because technological factors can handle tasks more efficiently. However, excessive use of technology can have some negative impacts (Barua et al., 1995; Strassman, 1990). These findings have implications for the use of BDA and online platform technologies. Although the BDA should be used appropriately in general situations, it may be used fully for low-complexity tasks. However, while it is difficult to consider the use of online platforms as a strategy to solve the problem of high business complexity, increased usage of BDA may contribute to improving organisational knowledge-creation capabilities.

Limitations and Future Research

The limitations of this study are as follows. First, the control variables in our regression model are statistically insignificant. There are two possible explanations. First, those control variables are statistically significant when the dependent variable is a traditional concept of organisational performance such as profit or patent. Different from that, our research model chose ‘organisational knowledge-creation capability $(C A P_{i} = \frac{Score of collective {intelligence}_{i}}{Score of {groupthink}_{i}}) .$ So, it is natural that the use of other dependent variables seems to have influenced the significance of the control variable. Another presumption is that the organisation to which survey respondents belong is heterogeneous. This part may also have contributed to diluting the influence of the control variable.

Second, despite the various approaches, there is no plausible way to quantify the level of collective intelligence. This problem exists in groupthink studies as well. Both groupthink and collective intelligence are difficult to quantify because they require both long period and internal observation. Therefore, this study considered alternative ways to measure them through the external proxies suggested in the previous studies. To provide a more elaborate analysis of the organisational knowledge process, future studies should focus on ways to quantify organisational capabilities, such as collective intelligence.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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