ACCEPTANCE OF CONTEMPORARY TECHNOLOGIES FOR COST MANAGEMENT OF CONSTRUCTION PROJECTS

SUMMARY: The construction industry has become more digital and the traditional methods of construction activities are gradually becoming outdated. In this era of digital construction, various information and communication technologies have been developed and deployed to the site for the management and control of construction activities including cost management. Irrespective of the benefits of adopting these technologies, most of them are still not readily accepted for use for construction management. This study articulated Seven (7) recent technologies driving the industry and evaluated their acceptance for cost management of construction projects. The technologies include mobile technology, Augmented/Virtual Reality (AR/VR), Building Information Modeling (BIM), Internet of Things (IoT), Autonomous Equipment (Drones and Robotics), Artificial Intelligence (AI), and Predictive Analytics (PA). Data was gathered using a restructured questionnaire and technology acceptance model analysis was performed to identify which of the technologies have higher acceptance for cost management based on the criteria of availability, affordability, frequency of use, usefulness for cost management, and acceptance in the industry. Test statistics using Spearman’s correlations and Kendall’s correlations for each of the technologies and Spearman’s Correlations of Technology acceptance with other variables in the TAM Model were performed. The results showed that mobile technology has higher correlation values than other technologies, and therefore has a higher acceptance for cost management. Kendall’s coefficient of concordance values and Spearman’s correlation values for Mobile technology were all above 0.6 which indicates a high level of agreement among the raters and strong relationships between the compared TAM variables.


INTRODUCTION
The construction industry has embraced technology like other allied industries to manage its operations for better efficiency and accuracy. Information and communication technology (ICT) adoption in the construction industry cuts across all construction operations and activities. One of the operations of the industry that determines the success of a construction project is the effective management of construction costs. Management of Construction cost is the most important function for project success (Girma and Alemu, 2018). However, the cost management practice in present-day construction is becoming more complicated due to the ever-increasing complexity, and dynamic nature of modern-day constructions. Construction projects are becoming more competitive, complicated, and difficult to handle, and conventional approaches may not be able to solve such serious issues (Vasista and Abone, 2018). Construction firms have an unusually complex, fragmented, and unique set of market relationships and approaches to deal with (Moshood et al., 2020). The construction industry's project-based design, the highly complicated coordination system on projects, and the vast volume of data general contractors deal with, all highlight the need for productivity in project management (Holt, Benham, and Bigelow, 2015). All construction phases require an efficient exchange of information and collaborations among all project participants, such as project managers, consultants, engineers, site supervisors, workmen, suppliers, and subcontractors. The global advances in digital technologies gave rise to the development of different technologies to tackle construction operations, collaborations, and exchange of information, ranging from the simplest task to the most complicated. Construction planning and control, cost control and financial planning, computer-aided facilities management, and other operations all benefit from the use of information and communication technology, which gives the industry a variety of options for more effective and efficient project execution. The importance of ICT in construction project cost control cannot be overstated, but the majority of applicable ICT for cost management is still not widely adopted. Though various technologies have been deployed for use in executing construction activities including cost management, there is still an underlying gap as the problems of cost overrun are still inevitable.
Despite the fact that technology has streamlined the construction process, it has yet to achieve the efficiency gains that are necessary due to a lack of integration between applications (Holt, Benham, and Bigelow, 2015). Approximately 40 years ago, information and communication technology (ICT) was introduced into the construction industry, dramatically changing the industry's conventional paradigm into one of quicker and more advanced processes (Afolabi et al., 2017). These technologies are currently being deployed for construction project delivery. However, a number of factors work against the use of these technologies in the construction industry, the most significant are budget constraints for investment, a lack of knowledge about the profitability nature of ICT investments, high costs of hiring professionals, and a lack of staff with adequate Technological skills and knowledge, as well as the cost of training professionals (Ojelabi et al., 2018). These aforementioned factors lead to contracting organizations reverting to the conventional methods. Beside the factors, the main issue is users' reluctance to ICT adoption, because it is the actual ICT user who uses ICT resources to optimize work processes, and moving from a paper-based to an entirely automated system requires users to immediately embrace and use ICT (Moshood et al., 2020). However, some technologies are adopted faster than others (Yapa et al., 2019). While these technologies have been identified to be relevant for managing construction costs, some of these technologies are not readily adopted for this operation. This study evaluates the acceptance of these technologies by the cost managers to identify which of the technologies are readily accepted in the industry for cost management in presentday construction.

The Contemporary Technologies Relevant in Construction Industry
There are technologies relevant to every aspect of cost management. These ICT technologies aid in improving client satisfaction, reducing teamwork mistakes, and increasing project participant awareness in terms of better service delivery and requirements (Vasista and Abone, 2018). Various technologies have gained good footage in the delivery of construction activities including project cost management. Various technologies have been in existence in the industry but this research reviewed the most current in the present fourth industrial revolution (IR4.0). The fourth industrial revolution started in 2011 and gained a wider audience in 2016 according to an article written by Liao et al., (2018). Hence, this research referenced the review of the technologies from 2016, capturing the key technologies that are driving the industry from the year 2016. The review identified the seven technologies that are currently being used in the industry for different construction activities. The seven technologies referenced from 2016 to 2019 include; Building Information Modelling (BIM), Augmented and Virtual Realities (AR & VR), Mobile Technology, Internet of Things (IoT), Artificial Intelligence and Machine Learning (AI & ML), Drones and Robotics, and Predictive Analytics. The various areas of application of the identified technologies in the industry were articulated and the core benefits and application for cost management were identified as highlighted in Table 1. However, despite the applicability of these modern technologies in any industry, their acceptability in the construction industry is still subject to questioning (Ojelabi et al., 2018).  (Nigam et al., 2016;Fadason et al., 2018;Cepurnaite et al., 2017;Jununkar et al., 2017;Haron et al., 2017;Chu et al., 2018;Kulkarni and Mhetar, 2017;Jones, 2019;Abanda et al., 2018;Tahir et al., 2018;Nsimire Mushamalirwa, 2016;DU, 2017;Gerbert et al., 2016;Perera et al., 2018;GenieBelt, 2018;Pickup, 2018;Agarwal et al., 2018;Mahami et al., 2019;Pistorius, 2017;Kapliński, 2018;Budiac, 2018b;J.Adwan and Al-Soufi, 2016) (Ahmed, 2019;Chu et al., 2018;Jones, 2019;Gerbert et al., 2016;Higgins, 2019;Rhumbix, 2018;GenieBelt, 2018;Pickup, 2018;Alsafouri and Ayer, 2018;Pistorius, 2017;Zhezherov, 2018;Santos, 2018;Sharifi, 2018;Thompson, 2019;GenieBelt, 2017;Budiac, 2018b;J.Adwan and Al-Soufi, 2016) 3

Predictive Analytics
• Construction data analysis and prediction • Enhances decision-making process • Tracking and analysis of construction problems for remedial actions • Construction cost estimation • Predictive analytics is a great tool for estimation, budgeting, and cost forecasting. (Higgins, 2019;Rhumbix, 2018;Pistorius, 2017;Bobriakov, 2019)

Applications Of Contemporary Technologies For Construction Cost Management
The features of the identified technologies have great applications in cost management of construction projects. Project Cost Management is a management activity that deals with forecasting, planning, control, cost finding, analysis, and evaluation of the contractors and it is used to control project cost (Miri and Khaksefidi, 2015). It is the systematic process of developing, monitoring, and adjusting a budget to achieve the maximum amount of work at a given level of quality in situations where unknowns and uncertainty may cause costs to rise beyond acceptable levels (Chigara et al., 2013). Specifically, cost management "identifies, collects, measures, classifies, and reports information that is useful to managers for determining the costs of products, customers, and suppliers, and other relevant objects and for planning, controlling, making continuous improvements, and decision making" (Kujala et al., 2014). Most of those procedures involved in planning, forecasting, budgeting, financing, funding, managing, and monitoring costs are included in project cost management so that the project can be completed on time and within the approved budget. (Igwe, Mohamed, and Azwarie, 2020). At every stage of the cost management processes, at least one of the contemporary technologies can be useful and applicable.
Building information modelling BIM for example is applicable in cost planning, cost estimation, scheduling, and cost control (Pučko et al., 2014;Ying, 2019;Kulkarni and Mhetar, 2017). Handling the project cost data by BIM approach give an opportunity to manage the construction project costs more efficiently (Pučko et al., 2014). Application of BIM for cost management of construction projects helps in the elimination of unbudgeted changes on projects, cost estimation accuracy to within 3%, and up to 80% reduction in time taken to generate a cost estimate (Kulkarni and Mhetar, 2017).
The augmented and virtual realities is applicable real-time tracking of time and cost of construction projects (Zaher et al., 2015). AR and VR are applicable in project planning, automated measurements, project modifications, onsite project information generation, and project team collaborations for effective communication (BigRentz Inc., 2019). AR allows teams to conduct walkthroughs of the entire project before it is executed, closely examining the details and components of the structure. This foresight can prevent schedule delays, reduce cost overruns, and identify any mistakes before they surface.
Mobile Technologies such as tablets, smartphones, and mobile intelligent hotspots are becoming increasingly important and essential tools for collaboration and bottleneck elimination in the three phases of construction: planning, designing, and construction (Kiganda, 2017). The majority of the important tools used in construction project management have been virtually translated into mobile devices such as tablets, smartphones, and other mobile devices in the form of mobile Apps. There are over 13,000 construction-related development and design smartphone apps in the market right now, according to estimates (Liu et al., 2017). Most of the mobile apps are applicable in cost estimating, scheduling, budgeting, onsite data gathering, effective communication and collaborations of project participants, and monitoring of construction costs.

The Internet of Things (IoT)
, is a network of interconnected computing devices, mechanical and digital machinery, items, animals, and people with unique identifiers (UIDs) and the ability to transfer data without requiring human-to-human or human-to-computer interaction (Rouse, 2019). IoT is applicable in data exchange in the construction industry, effective collaborations, and cost data management. IoT has the potential to provide time and money savings and better quality in construction works (Arslan et al., 2019).
When a machine duplicates human cognitive functions including problem-solving, pattern recognition, and learning, it is referred to as Artificial Intelligence (AI). Machine learning is a branch of artificial intelligence that employs statistical techniques to enable computers to "learn" from data without having to be explicitly programmed (Rao, 2019). AI and ML have been utilized in construction cost estimation, and cost modeling (Business, 2017). Some of the cost models include prediction of the life cycle cost using statistical and artificial neural network methods in conceptual product design, project cost estimation using principal component regression, web-based conventional cost estimates for construction projects using evolutionary fuzzy neural inference model and others. There is an enormous chance for AI different systems to be utilized for enhancing the effectiveness of project managers everyday job (Elrajoubi, 2019).
Drones, more accurately described in a business context as unmanned aerial vehicles (UAVs) are no longer limited to commercial activities, and they now demonstrate a wide range of business value-adding capabilities. The potential business applications are numerous, and they present organisations in the engineering and construction industries with disruptive prospects. By enabling remote sensing, actuation, and predictive capabilities, they can significantly extend human operations. These skills provide crucial benefits such as cost reduction, risk mitigation, and quality improvements, giving their adopters a competitive advantage. The various areas of applications of drones include automation of simple tasks which reduces labor costs significantly, while providing better accuracy through the use of multiple sensors on the same platform, and Real-time project cost and progress monitoring (Gregory and Laurent, 2016). Sending a drone to evaluate a job site saves time, and cost and keeps the technician on the ground rather than mounting scaffolding and managing the dangers of a construction site (Higgins, 2019). In a similar way to drones, the construction sector is integrating robots and autonomous equipment (Budiac, 2018).
Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. For the construction industry, the ability to capture realtime data and transform it into actionable information for forecasting has become a game-changing solution (Bobriakov, 2019). Predictive analytics is very much applicable in forcasting construction cost which is a principal component of cost management.
The use of information and communication technologies for project delivery obviously offers great benefits which cannot be overemphasized. However, one of the key determinants of ICT usage in the industry is the technology acceptance by the key players in the industry. There are many models and frameworks to explain user acceptance of new technologies but the technology acceptance model (TAM) is a popular theoretical framework (Yapa et al., 2019).

Technology Acceptance Model (TAM)
TAM is a well-known model that relates to technology acceptance and usage. It was first suggested by Davis in 1986 (Tantiponganant and Laksitamas, 2014;Elshafey et al., 2020). TAM has proved to be a theoretical model for explaining and predicting information technology user behavior. TAM serves as a foundation for determining how external factors affect creed, mindset, and purpose to use. Perceived importance and perceived ease of use are two cognitive views that TAM proposes. According to TAM, a user's true use of a technology system is determined by the user's behavioral intentions, attitude, perceived system utility, and perceived system simplicity, which is all directly or indirectly influenced by the user's behavioral intentions, attitude, perceived system utility, and perceived system simplicity. External influences, according to TAM, affect the intent and actual use of a product by mediating effects on perceived utility and perceived ease of use. Davis created the original technology adoption model in 1986, as shown in Figure 1 (Ma and Liu, 2011;Tantiponganant and Laksitamas, 2014).

FIG. 1: Original Technology Acceptance Model (TAM)
Perceived ease of use (PEOU), perceived usefulness (PU), attitude toward using (ATU), behavioral intention to use (BI), and actual device use were all part of the original TAM (AU). The two most critical determinants of system use are PU and PEOU (Wu and Wang, 2005). TAM has received considerable attention and empirical support and has been applied to many different end-user technologies (Ma and Liu, 2011). The validated TAM is illustrated in figure 2.

External Variables
Attitude Towards

Behavioural Intention to Use
Actual Use

FIG. 2: A validated Technology Acceptance Model
An extension of TAM by Venkatesh (2000) identified antecedents to perceived ease of use variable in the TAM model and grouped the antecedents into anchors and adjustments (Chuttur, 2009). The anchors were considered as beliefs about the technology, and the usage of the technology, whereas the adjustments were considered as beliefs that shaped direct experience with the target technology. Using this extension background, this research considered the Technology Availability (TAv), and the Technology Affordability (TAf) as the anchors while Frequency of Use (FU) of the technology was considered as the adjustments. Hence, the anchors and the adjustments were introduced as determinants of PEOU. Figure 3 illustrates the TAM model with the introduced antecedents.

FIG. 3: TAM model with the introduced antecedents
Five relationship exist based on the model illustrated in Figure 3 which include TAv_PEOU, TAf_PEOU, TFu_PEOU, PEOU_PU, and PU_TAcpt. Hence the following five hypotheses were proposed for each of the technologies; H1: Technology availability has a significant effect on the perceived ease of use H2: Technology affordability has a significant effect on the perceived ease of use H3: Technology frequency of use has a significant effect on the perceived ease of use H4: Perceived ease of use has a significant effect on the perceived usefulness H5: Perceived usefulness has a significant effect on technology acceptance The technology acceptance models (TAM) have been used in construction-related researches. Elshafey et al.,

Frequency of Use
Adjustment construction field that have utilized TAM. Hence, this research also employed the TAM to evaluate the acceptance of the contemporary technologies relevant in the construction with emphasis on the application for cost management of construction projects.

RESEARCH METHODOLOGY
Data was gathered using a Likert Scaled structured questionnaire and Quantity Surveyors were specifically the target respondents. The Quantity Surveyors were chosen because cost management of construction projects is their primary responsibility and they have the requisite knowledge on construction cost management. The questionnaire was administered online to Quantity Surveyors in Nigeria using Google Form. Nigeria was chosen for this study due to the rapid infrastructural developments recorded in the country at the time of this study. According to the National Bureau of Statistics, (2019), the Nigerian Construction sector grew by 66.99% in nominal terms (year on year) in the first quarter of 2019, an increase of 58.02% points compared to the rate recorded in the same quarter of 2018. This was also higher when compared to the rate recorded in the preceding quarter. The Google Form link was sent to the respondents through e-mails, social media platforms (WhatsApp and Facebook Messenger precisely). The data gathered using the google form was downloaded in Microsoft Excel (CSV format), coded, and entered into the Statistical Package for Social Sciences (SPSS V. 25) for analysis. A total of three hundred and forty-nine (349) responses representing 91.36% of the required 382 estimated sample size were gathered. Hence, 349 responses were used for data analysis. Their perception and acceptance of the identified technologies were evaluated in this study. The technology acceptance model (TAM) was the base for the analysis using the TAM criteria. The data gathered were tested for normality and the result showed that the data is not normally distributed, due to the number of the data used. Hence, non-parametric approach were used for the analysis. The two nonparametric statistics used included the Spearman's correlations and Kendall's coefficient of concordance. Spearman's correlations were used to evaluate the relationship between the TAM criteria for all the technologies, while Kendall's coefficient of concordance was used to check the level of agreement among the raters.

FIG. 4: Professional Qualifications of Respondents
Education was found to advance center task performance by giving people explanatory and procedural information with which they can finish their tasks effectively (Abdulrahamon et al., 2018). To ensure that the responses are reliable, the educational qualification of the respondents is of paramount importance as it will be an indication that they are well informed on the subject matter. The chart in figure 2 highlights the highest educational qualifications of the respondents. The least qualification of the respondents is university first-degree certificate which only 32% has as the highest educational qualification.  Figure 3 shows that only 27% of the respondents have professional experience of 5years and below. The remaining 73% have a professional experience above 5years, of which 24% have over 20years of professional experience. This means that the respondents have the requisite experience to give reliable information on the research area.

FIG. 6: Years of Professional Experience of Respondents
It is very important to ascertain the level of awareness of the respondents about the identified technologies in the construction industry. This can help to verify if the respondents have the requisite knowledge to give reliable information about the technologies. Figure 7 shows the responses of the respondents regarding their respective level of awareness of the identified technologies in the construction industry. It is obvious that a greater percentage of the respondents have at least a level of awareness for each of the identified technologies.

Analysis of the Identified Technologies
The reliability of the data gathered was checked and the Cronbach's alpha (α) value of 0.895 (Table 2) indicates good internal consistency. The external variables are the introduced antecedents, which their extent of influence on the validated TAM model variables was verified using Spearman's Correlations to check the strength of relationships (effect size) and Kendall's Concordance level to check the level of agreement. This research used Spearman's Correlations as the index of effect size to represent the empirical strength of a relationship between each pair of the constructs in TAM. A statistical measure of the strength of a monotonic relationship between paired data is Spearman's correlation coefficient. In a sample, it is denoted by ρ and is by design constrained as −1 ≤ ≤ 1 (Pirie, 2014, Shi andConrad, 2009). The relationships checked are as illustrated in the model in Figure 3 which include TAv_PEOU, TAf_PEOU, TFu_PEOU, PEOU_PU, and PU_TAcpt. Hence the following five hypotheses were tested for each of the technologies.    3 showed that all the hypotheses were supported except for the TEOU_TUCC for Internet of Things (IoT) and Predictive Analytics with significant levels of 0.027 and 0.767 respectively which indicated that the correlations were not significant at these levels. The correlations of each pair in all the technologies show levels of effect each variable has on the paired variable. Since correlation is effect size, the following guide can be used to verbally explain the intensity of the correlation. For the absolute value of τ and ρ; 0. 00-0.19 "very weak", 0.20-0.39 "weak", 0.40-0.59 "moderate", 0.60-.79 "strong", 0.80-1.0 "very strong" (Pirie, 2014, Liu et al., 2017and Gie and Fenn, 2019. Based on this guide, the levels of the effects in each pair were established with Mobile Technology having the highest level of effect in each pair of the variables. The absolute values of the correlations were all greater than 0.60 which means that the effect size of the variables was all strong for each pair of the variables. To further investigate the strength of effects of each of the TAM variables on technology acceptance, each of the variables was correlated with the acceptance of each technology. The test statistics were given in Table 4 and  Table 5 for Spearman's rho ρ correlations and Kendall's tau_b τ respectively. The correlation results showed that mobile technology has higher correlation coefficients with other variables when compared to correlation values of every other technology. This is in line with the findings of Liu et al., 2017 when they investigated the perceived benefits of mobile technology in New Zealand construction using Spearman's correlations. Though their investigation was centered on overall productivity in the industry, their finding showed that all the perceived benefits of using mobile technology have a positive relationship with overall productivity improvement. This is to buttress the point that mobile technology is very relevant and essential for construction management and control. The correlation coefficient of mobile technology acceptance with the affordability of the technology and the frequency of use is 0.498 and 0.564 respectively which indicates a moderate effect, which means that acceptance of mobile technology is influenced by its affordability and frequency of use, and vice versa. Other variables such as technology availability, technology ease of use, and the technology usefulness for cost control also have strong and very strong correlation values of 0.614, 0.676, and 0.853 respectively.  Kendall rank correlation is a non-parametric test that determines how closely two variables are related (Shi and Conrad, 2009). Kendall's Tau b is a popular statistic for describing the strength of the monotonic relationship between two variables. The values range between plus and minus one same way as Spearman's rho correlations. Mobile technology acceptance has higher dependence values on all the paired variables than other identified technologies and the level of dependence (Kendall's tau_b τ values) ranges from moderate to very strong (Table  5).

RESEARCH FINDINGS AND DISCUSSION
The test statistics for relationships between TAM variables for each of the technologies (  (Table 4 and Table 5) also indicated that the internal correlation values between technology acceptance for Mobile Technology and other TAM varaibles were all higher than the values for all other identified technologies. It can then be deduced from the values that Mobile technology has been generally rated higher than other technologies to be more readily available, easy to use, affordable, and useful for cost management compared to other technologies. This does not necessarily indicate that the potentials of other contemporary technologies for cost management were lower than that of mobile technology. The reason for the high rating for mobile technology is not only because of its capabilities for construction management operations, but due to its widely application in other spheres of life. The raters are well informed about the technology when compared to other identified technologies. Figure 7 explained that majority of the respondents were extremely aware of mobile technology than every other technologies. This explains why mobile technology has become an essential tool in the hands of all project participants, including artisans, at all project sites. The availability of mobile technology, and how it is easy to manipulate made the use more frequent. And since mobile technology is now a must-have in construction sites, several researches have been carried out on the applications of the technology for construction management and control. There are software and mobile applications available today to assist in the management of all aspects of a construction project (Jones, 2019). Budiac, (2018) reported that in September 2017, a total of 158 construction industry professionals from small to midsize (SMB) companies across North America were polled about their existing and potential technology and software use. According to the results of the study, 58% of SMB construction professionals state their company uses a smartphone or tablet-based applications at least "often.". Also unsurprising, hosted/cloud software users were more likely to depend on mobile. Also, Riddell, (2017) reported that in their survey, over 80% of construction professionals surveyed said mobile technology is a top priority, with 4 out of 5 saying they are using it. The convenience in making use of mobile technology for construction activities has drastically reduced paperwork with bulk data on record cards to a greater percentage. Cost management is a key assignment that demands accurate data management and due to the acceptability level of mobile technology in the industry, most of the cost management activities are gradually being translated into mobile apps to enhance accuracy. Liu et al., (2017) stated that Construction professionals' (CPs) workspaces are not limited to particular office locations, and since they are still on the move, mobile computing can provide them with a fast, convenient, and easy-to-carry platform for communicating pertinent on-site information to other stakeholders in different locations. Companies are becoming more aware of how mobile technology can simplify and automate the capture of information in the field and transmit the information back to company management systems, so mobile technologies are becoming more popular on the construction site (Casey Cline and Davis, 2013). Furthermore, mobile devices (such as smartphones and tablets) not only have communication and computing capabilities but also image and video recording capabilities that could be used for record-keeping and documentation. Based on this fact, construction managers should harness the potentials of mobile technology and as well make effort to also maximize the use of all other technologies for effective cost management.

CONCLUSION
Several technologies have been deployed to the site for the management of construction activities in the present era of digital construction. These technologies as already articulated play a vital role in enhancing the effectiveness and efficiency of operations. This article identified various contemporary technologies and their areas of applications for construction cost management. The article x-rayed these technologies to identify the top technology that is readily accepted in the industry for cost management operations. The criteria for the assessment were based on the technology acceptance model criteria which include; perceived ease of use, perceived usefulness, acceptance, and the extension criteria of availability, frequency of use, and affordability. The analyses have shown that mobile technology has gained a good acceptance level in the industry and was ranked first among the identified technologies. Mobile technology has gained applications in all construction operations including cost management of construction projects. Real-time data collection and transmission between the Jobsite and project managers in the back office is possible thanks to mobile technology. There are a plethora of constructionrelated mobile apps on the market right now, with features ranging from basic calculations to comprehensive architectural renderings.
Nearly 13,000 construction-related architecture and design applications are currently on the market and this figure is on the increase per day as several other applications are developed in different parts of the world, all addressing different and related construction issues at different stages. The available smartphone applications in the construction sector range from project management, calculators, safety, integrated construction cost and accounting, construction site operations, computer-aided design, 2D and 3D designs and drawings, estimating, and building information modeling (BIM). Therefore, the relevance and the applications of mobile technology in the industry for cost management cannot be over-emphasized.