JISIB-vol-12_Nr-1(2022) (3).pdf Journal of Intelligence Studies in Business Vol. 12 No. 1 (2022) Open Access: Freely available at: https://ojs.hh.se/ pp. 65–82 Mobile Applications Adoption and Use in Strategic Competitive Intelligence: A Structural Equation Modelling Approach Milind Thomas Themalil ABSTRACT This article examined the key determinants of mobile applications’ adoption and use in strategic competitive intelligence. A quantitative research based on a survey of 150 participants drawn from strategic competitive intelligence practitioners and analysts was used to examine and validate the extended UTAUT2 Model to identify the key determinants of mobile applications` adoption and use in SCI. PLS-SEM algorithm was used to analyse to the importance-performance map analysis that showed the greater absolute importance of cognitive psychological perceptive which this study addressed by examining key determinants of behaviour intention and user behaviour. KEYWORDS: Strategic Competitive Intelligence; UTAUT; UTAUT2; Adoption; Mobile 1. INTRODUCTION Competitive intelligence has become a global phenomenon in today`s environment that is characterised by global competition. Big data analytics, AI, IoT, 5G/6G, cybersecurity, as well as the adoption and use of mobile applica- Twitter, and Telegram have enabled high- speed availability, transfer, and analysis of large amounts of data collected and accumu- lated by individuals and organisations over the years (Maune, 2021). In the last decades, companies have invested resources dramati- cally in Competitive Intelligence (CI) systems, which enabled business users to discover their rich, reliable, and relevant data. CI is providing companies with the tools to make informed decisions. It is enabling com- panies to keep ahead of the competition and industry trends. The past decade has seen a tre- mendous growth in mobile applications usage the world over. By the end of 2020, reports estimated that there were about 3.5 billion 66 smartphone users worldwide (Maune, 2021). According to statista.com website, an esti- mated 1.4 billion smartphones were sold in 2020 alone. This has increased the demand and use of mobile applications by companies. What is not known, however, are the major key deter- minants for the adoption and use of mobile - minants have on behaviour intentions and use behaviour of mobile applications in SCI is still mystery. Thus far, CI research has focused primarily on the same phenomenon, how to gather infor- mation to make better decisions (Solberg, 2019 cited by Maune, 2021). Research is now start- ing to address CI from a business intelligence Intelligence this time around using algorithms as a predictive tool. Previously, CI research was more concerned with web and desktop applications but there is a rapid shift towards mobile applications due to information avail- able anytime, anywhere from everyone who has - enced by an increase in the number of mobile application and the number of active users per day (Maune, 2021). Mobile intelligence has now combined BI, transactions, and multimedia. Mobile applications have become the biggest companies that are ignoring mobile applica- tions for intelligence are doing so at their own peril. What is currently unknown is how deep they can be relied on by intelligentsia? What business leaders often fail to under- stand are the key determinants for the adop- tion and use of mobile applications in SCI? This usually serves as a differentiator among CI practitioners and analysts. With the devel- opment of a number of mobile applications and the increase in mobile penetration globally, it is critical for SCI practitioners and analysts to appreciate the key determinants for the adop- tion and use of mobile applications in SCI. Mobile applications have become the focal area for new ideas and big data analysis with more and more organisations turning to these plat- forms to map their strategies. In this dynamic world, business leaders need to know what their competitors are up to. Additionally, they need to gather the trends, patterns, and rela- tionships they see emerging across mobile platforms. The question that should be asked Mobile applications platforms have become new areas to look for business opportunities. CI is very important in this regard and should be prioritised to identify these opportunities. The aim of this study was to empirically examine and validate the proposed path anal- ysis model (Maune, 2021). The model was an extension of the UTAUT2. We analysed the data use of mobile applications in SCI. Behaviour intention and use behaviour from a cognitive psychological perspective was used. More spe- were; (i) to establish the key determinants for the adoption and use of mobile applications in intention on use behaviour in the adoption and use of mobile applications in SCI, and (iii) to develop a path analysis model suitable for the adoption and use of mobile applications in SCI. To achieve this, the authors adopted a pos- itivism research philosophy. The authors used a deductive research approach to gather data through an online survey sent to CI practi- tioners and analysts as well as those involved in decision making in various organisations. An explanatory research design assisted the researcher in examining the relationship between variables as well as assisting in iden- - - tionnaires were sent through different online - ings have both managerial and practical impli- - cal, societal, political, and educational. The remainder of the article will be as - dates the proposed path analysis model and the hypotheses will be followed by the research method used. This will address the research respondents and procedure, measurement, approach to SEM, analysis, model adopted, and the structural model analysis. Thereafter, dis- cussion of results will follow. The study`s impli- cations for research and practice as well as its limitations. The study conclusions will be given and the article will end with a reference list. 2. LITERATURE REVIEW In this section, the study presents an overview - tance and use of technology (UTAUT2) model 67 - cusses the new constructs that were added to the UTAUT2 (that is, perceived risk, trust, by Maune (2021). 2.1 Theoretical framework of Technology (UTAUT2) Based on a review of the extant literature, developed UTAUT as a comprehensive syn- thesis of prior technology acceptance research. UTAUT has four key constructs (performance - behavioural intention to use a technology and/ or technology use. We adapt these constructs to which using a technology will provide bene- - ities; is the degree of ease associated with consumers’ use of technology; is the extent to which consum- ers perceive that important others (for exam- ple, family and friends) believe they should use a particular technology; and conditions refer to consumers’ perceptions of the resources and support available to perform performance expectancy, effort expectancy, behavioral intention to use a technology, while behavioral intention and facilitating conditions determine technology use. Also, individual dif- ference variables, namely age, gender, and experience are theorised to moderate various above that was necessary to make the theory applicable to this context. or pleasure derived from using a technology, and it has shown to play an important role in determining technology acceptance and use such hedonic motivation (conceptualised as - ence technology acceptance and use directly Tam, 2006). In the consumer context, hedonic motivation has also been found to be an import- ant determinant of technology acceptance and use (Childers, Carr, Peck, and Carson, 2001; hedonic motivation as a predictor of consum- ers’ behavioural intention to use a technology An important difference between a con- sumer use setting and the organisational use setting, where UTAUT was developed from, is that, consumers usually bear the mone- tary cost of such use while employees do not. The cost and pricing structure may have a sig- - ularity of short messaging services (SMS) in China is due to the low pricing of SMS relative to other types of Mobile Internet Applications (Chan, Gong, Xu, and Thong, 2008). In market- ing research, the monetary cost/price is usually conceptualised together with the quality of products or services to determine the perceived as consumers’ cognitive tradeoff between the per- - etary cost for using it (Dodds, Monroe, and Grewal, 1991). The price value is positive when to be greater than the monetary cost and such price value has a positive impact on intention added as a predictor of behavioral intention to Prior research on technology use has intro- duced two related yet distinct constructs, namely and . Experience, as an opportunity to use a target technology and is typically operationalised as the passage of time from the initial use of a technology by categories with different periods of experi- ence. experience as three levels based on passage of time: post-training was when the system was initially available for use; 1 month later; as the extent to which people tend to perform behaviours automatically because of learning et al. (2005) equate habit with automaticity. conceptualised rather similarly, habit has been operationalised in two distinct ways: 68 Kim and Malhotra, 2005); and second, habit is measured as the extent to which an indi- vidual believes the behavior to be automatic (Limayem et al., 2007). Consequently, there are at least two key distinctions between expe- rience and habit. One distinction is that experi- for the formation of habit. A second distinction is that the passage of chronological time (expe- rience) can result in the formation of differing levels of habit depending on the extent of inter- action and familiarity that is developed with months, different individuals can form various levels of habit depending on their use of a tar- perhaps what prompted Limayem et al. (2007) to include prior use as a predictor of habit; and likewise, Kim and Malhotra (2005) controlled for experience with the target technology in their attempt to understand the impact of (2005) also noted that feedback from previous consequently, future behavioral performance. In this context, habit is a perceptual construct in technology use have delineated different technology use. Related to the operationalisa- tion of habit as prior use, Kim and Malhotra (2005) found that prior use was a strong pre- dictor of future technology use. Given that there are detractors to the operationalisation of habit as prior use (see Ajzen, 2002), some work, such as that of Limayem et al. (2007), has embraced a survey and perception-based approach to the measurement of habit. Such an operationalisation of habit has been shown to directly affect technology use over and above the effect of intention and moderate the effect of intention on technology use such that inten- tion becomes less important with increasing in the context of other behaviors have been reported in psychology research (see Ouellette and Wood, 1998). UTAUT2 Model. 69 2.2 Conceptual framework 2.2.1 Identifying constructs to incorporate into UTAUT2 This section presents an overview of the four constructs that were added to UTAUT2 and discusses them in detail (see Maune, 2021). The constructs are perceived risk, trust, sub- complements the UTAUT2 constructs as given through a literature review carried out by Maune (2021). The conceptual framework developed in the previous study (Maune, 2021) formed the basis of the current study. In tech- nology acceptance and use, perceived risk and trust have proven to be strong predictors of behavioural intention (see Maune, 2021). Risk has been considered a strong driver of behavioural intention and use behaviour of mobile applications. Recent developments in the operations of big technology companies have caused risk and trust to be amongst the strongest predictors of behavioural inten- tion and use behaviour of mobile applications in gathering SCI data. The use of mobile appli- cations in SCI gathering has become popular recently. Technology developers are coming up with useful tools to gather SCI data from mobile application platforms. The platforms among others. These platforms are proving to be rich mines for SCI. borrowed from the Theory of Reasoned and the Theory of Planned Behaviour (TPB) other reasoned action models is the idea that behaviour is guided by intentions (Ajzen, 2012). Subjective norms are the individual’s he or she should engage in the behaviour and are assumed to capture the extent of per- ceived social pressures exerted on individuals to engage in certain behaviour. O’connor and Armitage (2003) argue that subjective norms are a function of normative beliefs. To them, normative beliefs represent pressures that are and friends with respect to the behaviour in question. Normative beliefs and the personal motivation to comply with such beliefs and (O’connor and Armitage, 2003). With respect to - tive norms represent actors’ perceptions about pressures generated from important signif- icant others with respect to the behaviour (Chatzisarantis and Biddle, 1998). Measures of subjective norms also respect a personal tendency to comply with pressures to the self-determination theory, psychological events that include compliance and pressure, represent control, and therefore, it is argued that subjective norms cover only the controlling dimension of personal experience. The subjec- tive norm is also based on salient beliefs, called normative beliefs, about whether particular referents think the respondent should or should not do the action in question (East, 1993). East (1993) further argues that like expected values, measures: , the likelihood that the referent holds the normative belief, and the moti- vation to comply with the views of the referent. Thus imi is the determinant of the subjective norm. According to the TPB model, subjective norms predict the intention, which in turn pre- dicts use behaviour. Subjective norm is a strong 2015). According to Bandura (1997), - cacy refers to beliefs in one`s capabilities and knowledge to organise and execute the courses of action required to produce/perform certain behaviour/attainments. Studies by Bandura - dictor of behaviour and behavioural change. - by its effect on perseverance. The more people believe that they have the capacity to perform an intended behaviour, the more likely they are to persevere and, therefore to succeed (Ajzen, 2012). A considerable body of research attests motivation and performance (see Bandura and Locke, 2003). Subjective norms are used to com- used to complement performance expectancy and effort expectancy. Research by Roy (2017) were strong predictors of behavioural inten- tion and use behaviour in mobile applications. 70 related models hinge on intentionality as a key underlying theoretical mechanism that drives behaviour. Many, including detractors of this class of models, have argued that the inclusion of additional theoretical mechanisms is import- These constructs have become critical in the recent past in determining the adoption and use of mobile applications in SCI gather- ing. With SCI taking major strides in helping companies achieving sustainable competitive advantage, mobile applications have become Based on the study by Maune (2021) as well as the above explanations, perceived risk, 2.2.2 Hypothesis development This section presents the hypotheses that were developed to validate the proposed model in the review of theoretical and empirical studies in the sections above. These hypotheses are to validate and test the proposed path analysis model by Maune (2021). Therefore, we hypoth- esised the following: H1. The greater the individual`s perfor- mance expectancy regarding mobile apps use, the higher the level of behaviour intentions to use mobile apps in SCI. H2. The greater the individual`s effort expectancy regarding mobile apps use, the higher the level of behaviour intentions to use mobile apps in SCI. H3. The greater the individual`s social the level of behaviour intentions to use mobile apps in SCI. H4. The greater the facilitating conditions are perceived as favourable to mobile apps use, the higher the level of behaviour intentions to use mobile apps in SCI. H5. The greater the hedonic motivation is perceived as favourable to mobile apps use, Research model. 71 the higher the level of behaviour intentions to use mobile apps in SCI. H6. The greater the price value is perceived as favourable to mobile apps use, the higher the level of behaviour intentions to use mobile apps in SCI. H7. The greater the individual`s habit regarding mobile apps use, the higher the level of behaviour intentions to use mobile apps in SCI. H8. The greater the subjective norms are perceived as favourable to mobile apps use, the higher the level of behaviour intentions to use mobile apps in SCI. H9. The greater the individual`s self-ef- the level of behaviour intentions to use mobile apps in SCI. H10. The greater the perceived risk is seen as favourable to mobile apps use, the higher the level of behaviour intentions to use mobile apps in SCI. H11. The greater the individual`s trust regarding mobile apps use, the higher the level of behaviour intentions to use mobile apps in SCI. H12. The greater the individual`s behaviour intentions to use mobile apps, the greater the likelihood of the individual`s use behaviour of mobile apps in SCI. 3. METHOD This article targeted SCIPs and analysts as well as those in decision making. This study was conducted in the context of mobile appli- cations use in SCI. All applications that can be downloaded from application stores such as Play Store and App Store among others were evaluated within the scope of mobile applica- tions. These applications have made it easy for individuals and organizations to access large amounts of data. Mobile applications have both increased and strengthened the role of SCI in decision making globally. They have become big data mines for gathering intelligent infor- mation for decision making in competitive environments. 3.1 Respondents and procedure via email and WhatsApp platforms to SCI prac- titioners and analysts. The questionnaire was generated was then sent to the respondents. The survey needed approximately 15 to 20 min- utes to complete. Before this, a pilot question- CI knowledge to elicit salient features, ambigu- Such questions were deleted or rephrased in the main questionnaire. Completed question- naires were returned, automatically through the Google forms platform to the correspond- ing author by 98 respondents (65.3%). After cleaning the data, that is, removing observa- tions with missing data, and suspected unen- gaged respondents, 96 (64% response rate) were retained for analysis. The sample size used was guided by Marcoulides and Saunders (2006). In this study, unengaged respondents response for all consecutive items (for exam- ple, a 7 throughout all the observed variables). Table 1 denotes the demographic descriptive statistics of the study. Variable Category Fre-quency Per- centage Gender Male 74 77% 22 23% Age <20 - - 21 – 30 12 12.5% 31 – 40 37 38.5% 41 – 50 11 11.5% >50 36 37.5% Experience Up to 1yr 9 9.4% 1 to 2yrs - - 2 to 3yrs 5 5.2% 3 to 4yrs 4 4.2% 5yrs or more 78 81.2% Education - - College - - Bachelor`s Degree 1 1% Master`s Degree 55 57.3% PhD 40 41.7% 3.2 Measurement This article adapted the measurement scales from prior research (Table 2). The latent vari- ables and the measurement items are as given in Table 2. The scales for the UTAUT2 con- structs, that is, performance expectancy, effort - tions, hedonic motivation, price value, habit, and behavioral intention were adapted from 72 and the scale for trust was adapted Groß (2015), while the scales for subjective norms, from Shneor and Munim (2019). All items were measured using a seven-point Likert–type scale, with the anchors being “completely disagree” and “completely agree.” Gender was coded using 1 or 2 dummy vari- ables where 1 represented men and 2, women. Age was measured in years, while experience was also measured in years. Use behaviour was measured using both scale and frequency of mobile applications use. The researcher created an online questionnaire using Google forms in English and was reviewed by university staff, SCIPs and university students for content valid- ity, completion time, and simplicity. The online selected individuals from the researcher`s WhatsApp professional groups who were not part of the main survey. Preliminary evidence showed that the scales were reliable and valid. Latent variable Measurement items Factor loadings Source PE ( PE2. Using mobile Apps increases my chances of achieving things that are important to me. PE3. Using mobile Apps helps me accomplish things more quickly. PE4. Using mobile Apps increases my productivity. 0.995 0.824 PE1-4 adapted and expectancy” in and EE ( EE1. Learning how to use mobile Apps is easy for me. EE2. My interaction with mobile Apps is clear and understandable. EE4. It is easy for me to become skillful at using mobile Apps. 0.819 0.848 0.798 from “effort expectancy” in SI (social SI1. People who are important to me think that I should use mobile Apps. should use mobile Apps. SI3. People whose opinions I value prefer that I use mobile Apps. 0.710 0.999 SI1-2. ( technologies I use. using mobile Apps. from “facilitating conditions” ( 0.914 0.959 from “hedonic motivation” in price value. 1.000 from “price value” in 1.000 et al. (2012). 73 PR ( PR1. I would not feel completely safe to provide personal information through mobile apps. PR2. I am worried about the future use of mobile apps platforms because other people might be able to access my data. information via mobile apps platforms. PR4. The likelihood that something wrong will happen with the mobile apps platforms is high. 0.782 0.945 0.819 (2016). TT TT1. I think they are honest. TT2. I think they are trustworthy. TT3. I think they provide good services to users. TT4. I think they care about their users and take their concerns seriously. TT5. I think they keep users’ security and privacy in mind. from “trust” in Groß (2015). SN SN1. People who are important to me think that I should use mobile apps in SCI. to use mobile apps in SCI. SN3. My colleagues think that I should use mobile apps in SCI. SN4. My friends think that I should use mobile apps in SCI. 0.827 0.864 0.917 from “subjective norms” in Shneor and Munim (2019). SE (self- platforms in SCI. SE2. I have the expertise needed to use mobile apps. mobile apps in SCI. platforms in SCI. 0.627 0.906 0.922 from “subjective norms” in Shneor and Munim (2019). BI ( BI1. I intend to continue using mobile apps in SCI in the future. BI2. I will always try to use mobile apps in SCI. BI3. I plan to continue to use mobile apps in SCI frequently. 1.000 from “behavioural intention” (2012). UB (use UB1. I frequently use mobile apps in SCI. UB2. I spend much effort in using mobile apps in SCI. FREQUENCY: Roughly estimating please indicate how many times have you used mobile apps platforms in SCI in the past year? (Please indicate the number of times). 0.890 0.887 1.000 from “subjective norms” in Shneor and Munim (2019). 3.3 Approach to structural equation modelling There are several distinct approaches to SEM This study adopted the approach by Maune, Matanda, and Mundonde (2021) the Partial Least Squares (PLS) using SmartPLS 3 soft- ware to analyse data. The PLS-SEM was used because of the small sample size and its pre- dictive accuracy. Despite its limitations, PLS- SEM is useful in applied research projects and sciences, marketing, organisation, manage- ment information system, and business strat- cleaned before imported into SmartPLS 3. 3.4 Analysis The PLS path modeling estimation for this - vations came out of the path analysis model: 74 - ple regression (Maune et al., 2021). As part of the measurement model evalu- ation, some items (see table 2) were omitted from the analysis due to high cross-loading and low factor loadings (<0.600) (Gefen and Straub, 2005). To test the reliability of the con- structs, the study used Cronbach`s alpha and composite reliability (CR) (Table 3). All the CRs were higher than the recommended value of each construct exceeded the 0.700 thresholds. Convergent validity was acceptable because for reliability and validity, along with the fac- tor loadings for the items are as shown in Table 3. Discriminant validity was assessed by Loadings VIF Cronbach`s Alpha Composite Reliability AVE PE1 0.995 2.400 0.866 0.909 0.834 PE3 0.824 2.400 EE1 0.819 1.459 0.760 0.862 0.676 EE2 0.848 1.683 EE3 0.798 1.538 SI1 0.710 1.856 0.809 0.855 0.751 SI2 0.999 1.856 0.914 2.380 0.865 0.935 0.877 0.959 2.380 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 SN1 0.827 1.599 0.841 0.903 0.757 SN2 0.864 2.550 SN3 0.917 2.637 SE2 0.627 1.349 0.785 0.866 0.689 SE3 0.906 2.203 SE4 0.922 2.075 PR1 0.782 2.980 0.833 0.887 0.725 PR2 0.945 3.546 PR4 0.819 1.528 BI1 1.000 1.000 1.000 1.000 1.000 UB1 0.890 1.506 0.734 0.883 0.790 UB2 0.887 1.506 BI EE HM HT PE PR PV SE SI SN UB BI EE 0.743 0.710 0.736 0.518 0.298 0.514 PE 0.471 0.548 0.554 0.010 PR 0.179 0.142 -0.138 -0.385 0.047 0.501 0.579 0.619 0.400 0.230 0.021 SE 0.810 0.720 0.603 0.499 0.105 0.056 0.443 SI 0.453 0.449 0.382 0.566 0.397 -0.102 0.404 0.458 SN 0.547 0.623 0.521 0.358 0.448 -0.151 0.639 0.570 0.699 UB 0.664 0.675 0.678 0.701 0.264 0.038 0.650 0.650 0.442 0.475 75 was greater than the inter-construct correla- 2015), with all values below the threshold of 0.900 implying the establishment of discrimi- nant validity (see Table 5). 3.4.2 Structural model - ity of the construct measures, the results of the structural model were evaluated. Maune et al. (2021) citing Tenenhaus et al. (2005) and Avkiran (2018) argue that the structural model analysis is done to provide supporting evidence to the theoretical model: “Where: is the endogenous construct and i represents the exogenous constructs, while jo is the constant term in this (multiple) regres- sion model, ij and j - tion condition applies.” hypothesised in the research framework. The structural model was assessed based on the R2, Q2 The goodness each structural path determined by the R2 value for the dependent variable, the value for R2 1992). The results in table 6 show that all R2 - 2 establishes the predictive relevance of the endogenous con- structs. Predictive relevance of the model is achieved when Q2 is above zero (0). The results - tion of the constructs (see table 6). The structural model was also checked for BI EE HM HT PE PR PV SE SI SN UB BI - EE 0.848 0.742 0.890 0.518 0.404 0.557 PE 0.350 0.604 0.528 0.267 PR 0.137 0.324 0.261 0.428 0.255 0.501 0.674 0.641 0.400 0.227 0.200 SE 0.825 0.886 0.623 0.562 0.373 0.232 0.510 SI 0.304 0.489 0.380 0.474 0.271 0.204 0.308 0.341 SN 0.574 0.770 0.575 0.396 0.533 0.345 0.703 0.630 0.675 UB 0.775 0.893 0.829 0.818 0.312 0.194 0.759 0.810 0.428 0.636 2, and Q2. Hypothesis Rel ationship STDEV T Statistics P Values 2.50% 97.50% 1 PE -> BI 0.724 0.348 2.083 0.037 0.414 1.727 2 EE -> BI -0.210 0.233 0.900 0.368 -0.595 0.197 3 SI -> BI -0.364 0.189 1.922 0.055 -1.108 -0.145 5 -0.246 0.352 0.700 0.484 -1.145 0.240 6 0.173 0.322 0.538 0.591 -0.192 0.972 7 0.503 0.191 2.637 0.008 0.252 1.148 8 SN -> BI -0.011 0.419 0.026 0.980 0.393 0.717 9 SE -> BI 0.865 0.425 2.036 0.042 0.562 1.873 10 PR -> BI 0.244 0.257 0.951 0.342 -0.324 0.658 12 BI -> UB 0.664 0.053 12.623 0.000 0.545 0.752 R2 R2 Adjusted Q2 BI 0.931 0.924 0.906 UB 0.441 0.435 0.344 76 77 of all sets of predictor constructs in the struc- tural model. The results in Table 3 show - nous constructs and corresponding exogenous values are clearly below the threshold of 5. Therefore, collinearity among the predictor constructs is not a critical issue in the struc- tural model. We therefore examined the results - as shown in Table 6. 3.4.3 Importance-Performance Map Analysis (IPMA) The IPMA was computed to determine the rel- ative importance of constructs in the PLS path analysis model. In this analysis, importance endogenous variable in the path analysis dia- latent variable scores. This analysis is partic- ularly important in prioritising managerial actions. It is critical for managerial focus to be directed at improving the performance of those constructs that exhibit a large importance regarding their explanation of a certain target construct but, at the same time, have a rela- tively low performance. In this case, a construct is more important if it has a higher absolute total effect on use - lute importance than any other constructs - mance if it has higher mean latent variable displays greater performance than any other 4. DISCUSSION The key determinants of mobile applications UTAUT2 model were examined. More empha- sis was placed on the cognitive psychological perspective of behavioural intention and use behaviour. Adoption and use of mobile appli- cations were considered planned behaviour. A path analysis model developed in the previ- ous study (Maune, 2021) was tested using PLS- SEM algorithm in SmartPLS software to ascer- tain critical paths and relationships. The results of the study are tabulated in Table 6. Of note, however, was the omission of latent variables et al., 2012; Groß, 2015). These latent variables were omitted because of high-cross loadings or low factor loadings (Gefen and Straub, 2005). The paths were, however, not supported by the data. In light of this, it is important for future studies to validate this using a bigger found otherwise. These paths were, however, not supported by the data. The following latent - despite previous research pointing otherwise 78 et al., 2016; Roy, 2017; Shneor and Munim, Chao, 2019; Tarhini et al., 2019; Khurana and - tistics. Consequently, the results were in line with various studies as shown in Appendix 2 relationships between the variables. The structural model was assessed for 2, Q2 of paths, with the results shown in Table 6. - Miller, 1992; Briones-Penalver et al., 2018). SCI practitioners and analysts relates to - agerial action is likely to bring the greatest improvement of a selected target construct in the PLS path analysis model. In this study SE proves to be critical for managerial action because of its highest total effect (0.574) (see - formance, it would be better for management to focus their efforts on SE, in the knowledge that it has a higher importance and its improve- ments is likely to lead to larger improvements in explaining UB. All else the same, a one unit rise in the performance of SE would bring about a 0.574 increase in the performance of UB (see Importance-Performance Analysis. Construct Performance Total effect BI 52.083 0.664 EE 46.889 -0.139 49.791 -0.164 64.410 0.334 PE 58.950 0.481 PR 69.406 0.162 42.448 0.115 SE 34.432 0.574 SI 50.923 -0.242 SN 44.228 -0.007 UB 46.303 - 4.1 Implications for research This study addresses the call of the previ- ous study (Maune, 2021) that emphasised the need to empirically examine and validate the proposed path analysis model/framework. This path analysis model was developed from literature as an extension of the UTAUT2 (see - lication is critical for CI analysts and practi- tioners given the amount of data that is kept and passes through mobile applications. This data will go a long way in mapping sustainable competitive corporate strategies. Results from this study have implications for further future research. Despite the popularity of the UTAUT2 in examining and testing relationships of con- structs in the adoption and use of technology, this study followed a different approach by extending the UTAUT2 framework. This was done by adding four other constructs borrowed from other theories (Maune, 2021). The pro- posed framework was examined empirically to determine key antecedents to behavioural intention and use behaviour of mobile applica- tions in CI. Through this approach, the study adhered to the cognitive psychological perspec- tive of human behaviour in decision making. - relation to BI and UB. - intention and use behaviour of mobile applica- tions in SC,I empirically. This gap in knowledge was uncovered in the previous article (Maune, 2021) that used literature review to develop a conceptual framework of behaviour intention and use behaviour of mobile applications in SCI. An extended framework was developed to identify key antecedents to behavioural inten- tion and use behaviour of mobile applications in SCI. Perspective antecedents in behavioural intention were given much attention in this study. The study validated these key anteced- ents to behavioural intention through PLS- SEM algorithm. Moreover, this study com- bined the UTAUT2 constructs with other four and trust) to examine their link with behaviour intention and use behaviour in SCI. Results were not far-off from previous studies as shown in Appendix 2 in Maune (2021). This study complements prior research that investigated relationships between UTAUT2, BI, and UB in this study hypothesises that performance 79 facilitating conditions, hedonic motivation, trust, and perceived risk were determinants of behaviour intention and use behaviour in SCI. not supported by the path analysis model. These by Maune, 2021) and support the idea that use behaviour is a planned behaviour (Shneor and - and UB (Liu and Tai, 2016; Barua et al., 2018; Chao, 2019; Tarhini et al., 2019; Khurana and Jain, 2019; Gharaibeh et al., 2020). 4.2 Implications for practice and BI, yet, performs poorly in explaining and deriving managerial implications, one is able to derive recommendations to drive BI and UB. The model has some key implications that are valid for SCI. practitioners and analysts relates to the fact important is the IPMA to managerial decision making. The IPMA helps management deter- mine important constructs in the PLS model. In this study the IPMA clearly shows import- ant determinants critical in the adoption and use of mobile applications in SCI. It is partic- ularly important in prioritising managerial actions. IPMA is helpful for managerial actions to be focused at improving the performance of those constructs that exhibit a large impor- tance regarding their explanation of a certain target construct. In this case, constructs with a relatively higher importance but a relatively low performance are particularly interesting for improvements and must be the focus of management. In fact, investing into the performance improvement of a construct that has a very small importance for the target construct would not be logical, since it would have lit- tle impact in changing (improving) the tar- get construct. In this study, SE is partic- ularly important for explaining the target construct, UB. In a ceteris paribus situation, a one-unit increase in the performance of SE increases the performance of UB by the value of the total effect, which is 0.574. At the same time, the performance of SE is relatively low, so there is substantial room for improvement. Consequently, in the PLS path model example, construct SE is the most relevant construct for managerial actions. 4.3 Limitations This article examined the key determinants of mobile applications` adoption and use in SCI using an extended UTAUT2 model. using online questionnaires which proved to be a challenge due to the cost of using internet and stress of being locked at home. Initially, the researcher had targeted 150 respondents but due to a number of reasons such as the one mentioned above, 98 responses were received. After the data cleaning process, only 96 were found suitable for use for the purpose of this study. Participatory methods may be planned, to include various groups in the study. A bigger A longitudinal study would also be useful in future studies that measure relationships between variables. In addition, future studies may extend the empirical analyses by consid- ering advanced PLS-SEM techniques such as methods to uncover unobserved heterogeneity and conclusions. Researchers are encouraged to consider a lot of research ethics to overcome challenges associated with the Covid-19 pandemic. Despite all this, the researcher had to forge ahead with what works, because truth is a normative con- cept – truth is what works. 5. CONCLUSION the relationships proposed in this study in extend academics` understanding of the key determinants of mobile applications adoption and use in SCI. The study placed more empha- sis on the cognitive psychological perspective of behavioural intention and use behaviour. - tion and use of mobile applications a planned behaviour. 80 To examine and validate the path analysis model developed by Maune (2021), the study fol- lowed a deductive approach with primary data collected through an online survey. The study applied the PLS-SEM algorithm to analyse relationships between latent and observed variables. Respondents were drawn from CI practitioners and analysts across the board. were sent via email and WhatsApp platforms. Completed questionnaires were returned auto- matically through the Google forms platform to the author by 98 respondents and after data cleaning process 96 responses were retained for analysis. - reliability tests such as Cronbach`s alpha, com- Monotrait ratio. Once the construct measures of the structural model were then evaluated. The structural model was assessed for good- 2, Q2 with the results shown in Table 6. demonstrated predictive relevance of the con- Briones-Penalver et al., 2018). Of importance, the path analysis model because the two paths were not supported by the model. This was SCI practitioners and analysts relates to - agerial action is likely to bring the greatest improvement of a selected target construct in the PLS path analysis model. In this study SE proves to be critical for managerial action because of its highest total effect (0.574) (see determine the relative importance of constructs in the PLS model. 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