A mobile business information system for the control of local and remote workforce through reactive and behavior-based monitoring

Expert Systems with Applications 42 (2015) 3462–3469

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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

A mobile business information system for the control of local and remote workforce through reactive and behavior-based monitoring Sergio Ríos-Aguilar a,⇑, Francisco-Javier Lloréns-Montes b a b

Engineering and Architecture Department, Pontifical University of Salamanca, Madrid, Spain Management Department, University of Granada, Granada, Spain

a r t i c l e

i n f o

a b s t r a c t

Article history: Available online 29 December 2014

This study analyzes the viability of using employees’ smartphones following the BYOD paradigm as a valid tool to enable firms to control effective presence (primarily of remote labor force). We propose a model for a Mobile Presence Control Information System with which to demonstrate experimentally the viability of unifying three elements that have only been examined individually in previous studies: the consumerization of ITs, the real geolocation capabilities of personal mobile devices that employees can use in the workplace, and the exclusive use of Mobile Web technology to obtain universal location information without the need to install native apps. We also propose a new and specific methodology to analyze the precision and accuracy of the location data obtained by smartphone geolocation services. We developed a prototype of the Information System proposed and demonstrated its validity under different real-use conditions, obtaining valuable information on the accuracy and precision of the location data from real devices (based on iOS and Android) under the conditions of heterogeneous connectivity representative of workplaces. This research enables us to establish a new framework for the requirements needed, on both quantitative and qualitative levels, for the accuracy of the mobile location systems that can be used in Presence Control Information Systems, particularly those related to control of remote labor force. Ó 2015 Elsevier Ltd. All rights reserved.

Keywords: Workforce control LBS Behavior-based HR control BYOD Consumerization

1. Introduction and definition of the problem It is now commonly accepted that companies must use Information Systems to gather and organize all information at their disposal to help the company’s business strategy to succeed. The increasing competitiveness of current market forces companies to pursue deeper understanding of the cause-effect relationship of their actions on profitability, making it necessary to have specific information that guides their process of improving competitive performance (Bradley & Nola, 1998; D’Aveni, 1994; De Assis Lahoz & Camarotto, 2012). Performance measurements related to time, quality, and productivity complement financial measurements and permit the introduction of improvements in operational processes. Analyzing the importance of time as a key factor in the performance of task completion, Ballard and Seibold (2004) identified ten dimensions of time in the workplace. Among them, lack of punctuality and absenteeism can be regarded as the most persistent obstacles ⇑ Corresponding author. E-mail addresses: (F.-J. Lloréns-Montes).

[email protected]

(S.

http://dx.doi.org/10.1016/j.eswa.2014.12.030 0957-4174/Ó 2015 Elsevier Ltd. All rights reserved.

Ríos-Aguilar),

fl[email protected]

affecting business competitiveness (Campbell, Ganco, Franco, & Agarwal, 2012). Early detection, evaluation, and rapid intervention are crucial when managing tardiness and absence in the workplace. These measures help prevent infractions from becoming a serious problem for the competitiveness of companies. Such detection usually requires investment in Information Technologies, among other tools for the acquisition and implementation of Control, Access, and Presence Systems. These items are often expensive, due not only to the initial costs (equipment for physical identification using card reader technology or biometric identification) but also to maintenance of the equipment and software that form the system’s back end, not to mention the possible cost of integration with pre-existing Information Systems (Kauffman, Techatassanasoontorn, & Wang, 2011; Sen, Raghu, & Vinze, 2009). This kind of system has also proven to be ineffective in extending control to mobile workforce, which in numerous private service sector enterprises may represent a high percentage of the staff, depending on the nature of the business. In this case what is needed is ‘‘proof of presence’’ at places and times established in advance (Kumar & Pandya, 2012).

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Implementing support for the mobile workplace by introducing Mobile Information Systems (mobile devices and applications engineered for the mobile environment), which enable control of the spatial and temporal dimensions of mobile work, grants not only a competitive advantage but also labor productivity growth to companies (Yuan, Archer, Connelly, & Zheng, 2010). An analysis of the literature shows see that a variety of research based on large-scale studies demonstrates that location-based queries are already a significant part of the total communication data sent from mobile devices. These results support the idea that such services are already sufficiently familiar to users of mobile devices, making their use easily translatable to the workforce, including both employees present in office and those away from the workplace. (Biancalana, Gasparetti, Micarelli, & Sansonetti, 2013; Ghose, Goldfarb, & Han. 2012; Pan, Nam, Ogara, & Lee, 2013). Some of the studies reviewed question the validity of the accuracy of the data obtained with location systems based on mobile devices. For example, Pulido Herrera, Kaufmann, Secue, Quirós, and Fabregat (2013) indicate that the global positioning system (GPS) is generally a significant aid but that it is not precise enough to locate a person. They do not, however, study either quantitatively or qualitatively the requirements for accuracy and precision that would be valid for locating a person, particularly for use in an information system for behavior-based control. The study by Subbu, Gozick, and Dantu (2013), on the other hand, indicates that the GPS system employed in mobile devices is being used widely for locating people on the street, but the authors conclude that the system does not work inside buildings due to the weakness of the signal and to interference, whereas the alternative—WiFi-based mechanisms—are available everywhere. Neither this study nor that of Pulido Herrera et al. (2013), which refers explicitly to the presence of multiple sensors on mobile devices, considers (a) the possibility of integrating data from multiple sensors into the location services of the mobile device, and (b) the quantitative study under real conditions of how combining data provided by different sensors to improve the quality of the position obtained in current smartphones would contribute to this accuracy. On another order, Song, Kim, Jones, Baker, and Chin (2014) indicate that locating the appropriate mobile application in an app store could become a difficult task for users and that ease of app discoverability is becoming a serious problem, both for users and developers of native applications and for the distribution stores themselves. Their study does not tackle the real possibility of providing location services using web applications instead of native apps, since web applications are universally available and independent of manufacturer or brand and model of smartphone. Heitkötter, Hanschke, and Majchrzak (2013) confirm this universality. Nayak, Swamy, and Ramaswamy (2013) indicate that the use of location-based applications on mobile devices has increased the risks to individuals’ privacy, and that monitoring an individual’s location and then integrating the data registered would enable one to reconstruct a profile that violates the fundamental rights of the user. The study by Li, Zhu, Gao, Chen and Ren (2014) explores the danger of exposing a smartphone user’s location data and indicates that the current focus is to develop mechanisms that protect privacy in location-based services. The study does not, however, tackle the possibility of permitting users’ explicit and nonintrusive use of these services. With all of the conditions presented, we believe that it is extremely important to study the viability of creating an Information System for the behavior-based control of workforce that enables non-intrusive use of technology for obtaining employees’ locations from their own smartphones, regardless of the device’s make and model.

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For this reason, our study proposes, first, qualitative and quantitative references for the quality of location information required in different environments to control the presence of remote persons, doing this by examining the strictest regulations in effect related to location-based services (primarily emergency services). We also analyze whether it is possible to fulfil the conditions suggested by these references using real smartphone-type devices, by testing the accuracy and precision of the location data obtained using the integrated location services under highly heterogeneous conditions representative of physical work environments. Finally, we propose an Intelligent Mobile Information System for Presence Control that uses only reactive, terminal-based location technologies in order to make the process of obtaining and delivering the worker’s location explicit and non-intrusive through use of a non-native mobile web app that guarantees universal access and contributes considerably to reducing overall costs. 1.1. Opportunities In the current economic context, it is of vital importance for a business to improve its competitiveness by rationalizing the necessary investment to achieve it. As highlighted above, having a Mobile Information System that permits rational and non-intrusive control of the workforce provides a direct and effective means of achieving such improvement. Two unique situations have been detected that can allow small and medium-sized enterprises (SMEs) to implement a presence control Information System for both local and remote workforce at very reduced cost and with minimal infrastructure: (A) The current maturity of mobile location technologies, using different transparent positioning mechanisms (A-GPS, GPS, WiFi and Cell-ID). (B) The growing BYOD (‘‘Bring Your Own Device’’) trend, which allows employees to use their own mobile communication devices (smartphones, tablets,. . .etc.) in the business as a complementary tool that plays a double role as both personal device for private use and provider of access to the company’s Information Systems. This paper builds on these foundations to attempt to ascertain whether it is feasible to implement an Information System for the behavior-based control of workforce that makes non-intrusive use of technology for obtaining the employees locations from their own smartphones, regardless of the device’s make and model. The study first analyzes the viability of using employees’ smartphones following the BYOD paradigm as a valid tool for companies to conduct presence control (primarily for remote workforce). It then proposes a Mobile Information System for Presence Control using exclusively terminal-based reactive location technologies, meeting cost minimization and universal access criteria. Subsequently, the paper proposes qualitative and quantitative references that meet criteria for the location information accuracy required in different business remote workforce control scenarios. Finally, this study discusses the results of testing the accuracy and precision of location data using real devices (iOS and Android) under heterogeneous connectivity conditions and workplace premises 1.2. Granularity in control of the workforce The use of monitoring technology can often lead to unwanted effects and behavior (Stanton, 2000), and continuous monitoring with mobile location technologies in particular increases such effects (Weckert, 2005). In this context, it is reasonable to expect an Information System designed to avoid these behaviors to use

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non-intrusive and reactive location technologies, thereby avoiding continuous monitoring (Ghose et al., 2012; Kumar Madria, Mohania, Bhowmick, & Bhargava, 2002; Zhou, 2011). To quantify the level of demand of accuracy suitable for locating the workforce, this study reviewed existing international regulations on the subject. It found that the United States Federal Communications Commission (FCC) E-911 mandate concerning the precision of the location of calls from mobile devices to Emergency Service E911 is the strictest and most specific set of regulations (Table 1). These regulations also provide a methodological framework for verification processes regarding compliance. At present, E-911 is the only regulation set that clearly quantifies the location accuracy required. The Information System proposed here will thus adopt the most restrictive location accuracy values from the aforementioned regulations as a valid working quantitative reference.

2. Information system and prototype Considering all conditioning elements, this paper proposes an Information System specifically oriented to business use for behavior-based control of remote workforce, using smartphones under the BYOD paradigm and focusing on universal access (device independence). A fully operational prototype of this Information System has been developed. It consists of a mobile web application with a control panel serving as a balanced scorecard and SaaS (Software as a Service, on the cloud). This prototype fulfills a double purpose of (i) serving as proof of concept for an Information System for the control based on behavior of remote workforce using a mobile web application, and (ii) allowing an empirical analysis of the current feasibility of its use under real conditions, using the strictest geographical location accuracy requirements obtained from mobile devices to emergency services established by the FCC for compliance to begin in 2019 (FCC 2010).

2.1. Description and components of the information system The proposed Information System for the behavior-based control of remote workforce consists of two discrete functional components: The first is a Mobile Web application to be accessed by the workforce using their smartphone mobile devices to clock in when arriving at the customer’s facilities or other installations deemed appropriate (Fig. 2). This application also allows employees to submit remarks concerning clocking that will be registered in the system at that moment (Fernández, Fernández, Aguilar, Selvi, & Crespo, 2013; Zhou, 2011). As to design, this study takes into account the various factors that affect customer perceived usability, as identified by Ho (2012) and Lee, Lee, Moon, and Park (2012). Technologies such as HTML5 and JQuery Mobile were chosen as the most cost-effective for companies and involve lower development costs because they require just one code base (only one production line) and can be deployed in almost all mobile devices that have a browser and Internet access (Heitkötter et al., 2013; Oliveira, Noguez, Costa, Barbosa, & Prado, 2013; Zhu, Chen, & Chen, 2013).

Secondly, a Control Panel Web Application for control of the remote workforce, accessible from any Web browser, allows realtime queries regarding workforce clock-in processes, including georeferenced information in maps and individual historical analysis of such processes for each employee. This individual historical analysis is in line with behavior analysis in the context of ubiquitous monitoring (Moran & Nakata, 2009; Pan et al., 2013). The proposed Information System has a client–server three-layered software architecture (see Fig. 1). This Information System was also modeled by taking into account the system proposed in the general model of location-based information system ‘‘Location Aware Mobile Services’’ (LAMS), tailored to terminal-based and network-assisted physical location of mobile devices. Since a core principle of the proposed Information System is ubiquitous access, all server components can be deployed on the Internet or within a corporate intranet as services provided using HTTP (Web). They thus remain accessible even if the corporation has perimetral security solutions or traffic filters (in this case, HTTP traffic is not usually restricted). All components used in the Server and Database layers in this Information System are open source software (LAMP platform: Linux OS, Apache Web Server, PHP, and MySQL database) to minimize costs involved in deployment of the proposed Information System. Another benefit of this approach involves scalability; both vertical and horizontal scaling of the LAMP platform are well known issues, and several proven architectures are useful when one must accommodate an increasing workload. 3. Location data analysis methodology An empirical analysis is performed to determine the location accuracy obtained with mobile devices using the developed mobile web application and thus to verify feasibility for business use as a mobile device for behavior-based control of the workforce and effective application of the BYOD paradigm. Although the accuracy of conventional GPS receivers is well documented for various devices (such as PNDs), study of the accuracy of devices with Assisted GPS (A-GPS), usually available in smartphones like those intended to be used in the proposed Information System, is less evolved. Due to hardware limitations, worse performance concerning location precision and accuracy is expected when using smartphones than when using conventional GPS devices (Zandbergen & Barbeau, 2011). To study the feasibility of using location data obtained from smartphones by means of a web app in the proposed Information System, sets of GPS location data under real conditions were collected. The location data obtained were compared to a real known location (‘‘truth point’’) to obtain an absolute accuracy measurement. 3.1. Instrumentation and data collection The GPS location data quality tests were performed using two of the most widespread smartphone mobile terminals currently on the European market: Samsung Galaxy S4 and Apple iPhone 5. These devices are also highly representative, since they belong to two of the most prevalent smartphone groups (those corresponding

Table 1 Mobile device location accuracy requirements from which emergency service E-911 is requested. Source: Adapted from FCC (2010). Location type

Required accuracy

Date regulation goes into effect

Mobile terminal based

50 meters for 67% of calls and 150 meters for 80% of calls 50 meters for 67% of calls and 150 meters for 90% of calls

Already in effect (since 18/1/2013) Scheduled to go into effect on January 1, 2019

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Fig. 1. Information system architecture for behavior-based control of the workforce.

to the Galaxy and iPhone trademarks), groups that have the biggest market growth and are evolving through constant improvements to their hardware specifications. Samsung Galaxy S4 uses a Broadcom BCM47521 GPS receiver chip with active suppression of sources of interference. The device used in the tests has an Android v4.4.2 Operating System. The Apple iPhone 5 uses a Qualcomm WTR1605L transceiver chip, and the operating system used was iOS 7. Both mobile devices registered geographical positions as decimal degrees in the WGS-1984 datum, GPS original geodetic reference system with direct equivalent to the European ETRS-89 used in most topographic or cartographic applications (IGN 2013). In both cases, the tests were performed with the GPS circuits activated and using networkassisted mode (A-GPS). WiFi-based location mechanisms were also active to help increase the accuracy (Soria Morillo, Ortega Ramírez, Alvarez García, & Gonzalez-Abril, 2012). The tests were carried out strictly during standard business hours, at random times between 8 h and 17 h, times representative of daily workforce activity times on workdays. No value of Horizontal Dilution of Precision (HDOP) was taken into account to complete the observations and no planning of field mission prior to the test conducted, in order better to reflect workforce use of the Information System under real conditions. The geographical coordinates of this point (446737 m, 4114616 m UTM zone 30S, European Datum 50) were obtained using a topographic grade sub-meter accuracy professional GPS receiver Topcon Hiper + with 10 mm + 1.0 ppm horizontal accuracy, properly calibrated and using a Differential Global Positioning System (DGPS). The mobile devices registered location data in WGS-84 format at intervals of 3 min, generating data sets of 25 samples each. Data were gathered on different days in 10 sets of samples, totaling 250 measurements for each study group.

3.2. Data analysis To assess the accuracy of both devices used in gathering the data, the variability in the data were contrasted with a previously determined real value corresponding to the observation point (truth point). To determine the possible normal distributions of the data sets (variable: accuracy—planimetric or horizontal error), several tests were conducted: Kolmogorov–Smirnov, Ryan-Joiner (similar to Shapiro–Wilk) and Anderson–Darling (see Table 2).

Once again, the result is proven similar for the three tests conducted, which provide p-values of less than 0.05, thereby confirming with 95% reliability that the hypothesis of normality for horizontal or planimetric accuracy is rejected for both data origins, Galaxy S4 and iPhone 5. After verifying that horizontal error distributions (for accuracy) are not normal, the non-parametric Mann–Whitney U test was used to contrast whether both data origins had the same distribution. The result obtained was a statistical value W = 49337.4. This test is also statistically significant at 0.0000 (p < 0.05), thus also rejecting the hypothesis of equal distributions of horizontal error (planimetric accuracy) for both populations with 95% reliability. The data analysis shows that the average values of the precision variable and the accuracy variable differ significantly for both mobile devices. It has also been shown that deviations are significantly different for both data origins. Distributions are different not only in precision but also in accuracy for both populations (Android and iOS data). Both mobile devices provide data of sufficiently high quality, and the mean of their values is close to the real value, but a greater data dispersion in found for iOS devices. 4. Results Fig. 3 shows the horizontal error (in meters) of all samples collected. As demonstrated above, this result confirms that the variability of location data collected by the iPhone device around the well-known spot (truth point) is greater than the data set collected with the Android device (See Fig. 4). The data collected will thus be analyzed quantitatively, calculating the horizontal accuracy by taking the root mean square of the errors (RMSE) of each data origin, first for each component and then by calculating the planimetric or horizontal value

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u n u1 X u1 X 2 t RMSEX ¼ exi RMSEY ¼ t e2 n i¼1 n i¼1 yi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RMSE ¼ RMSE2X þ RMSE2Y

ð1Þ ð2Þ

4.1. Accuracy results for the android device The planimetric RMSE error value obtained is 17.44 m for the horizontal component (XY). This value means that, for 67% of the

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GPS signal propagation and shifting HDOP values. These conditions and values were intentionally unplanned in the tests conducted. 4.2. Accuracy results for the iOS device The planimetric RMSE value obtained was 33.74 m for the horizontal component (XY). This value means that, for 67% of the observations made with the iOS device, the accuracy will decrease at 23.31 m. In this case, the value for the iPhone 5 device using the mobile web application is 2DRMS = 46.62 m. It might be concluded from the samples taken in this test that, at a 95% confidence level, the accuracy obtained with the mobile web application using the GPS of the iPhone 5 device falls within a 46.62 m radius of the real location value in an urban environment with demanding conditions for GPS signal propagation and shifting HDOP values, which were again intentionally unplanned in the tests conducted. 5. Verification of compliance with FCC rules The FCC proposes a method for determining whether a set of location errors demonstrates compliance with the accuracy requirements of location data, using order statistics. The FCC’s confidence intervals for accuracy standards can be selected with 90% confidence based on the number of samples (FCC 2000). Generally, when  the number of measurements is n,  the rth and sth largest measurements are xr and ys, respectively  x and y are the percentiles associated with probabilities p1 and p2, respectively, then the probability that x is less than xr, while simultaneously y is less than ys, is given by the formula confidenceðx  xr ;y  ys ; n; r;s; p1 ;p2 Þ   r1 X r1  X cn cn  i i ¼ p1 ðp2  p1 Þj1 ð1  p2 Þnj i nj i¼0 j¼1

Fig. 2. Login screen requesting the user’s credentials to access the Information System. Table 2 Results of the normality tests of samples for accuracy from both data origins. Data origin device

Samsung Galaxy S4 (Android)

Normality test

Statistic

p-Value

Kolmogorov–Smirnov Ryan-Joiner Anderson–Darling

0.081 0.882 3.927

<0.010 <0.010 <0.005

Apple iPhone 5 (iOS)

Kolmogorov–Smirnov Ryan-Joiner Anderson–Darling

Statistic

p-Value

0.157 0.904 7.721

<0.010 <0.010 <0.005

In this particular case, p1 = 0.67 and p2 = 0.95. From this expression, the upper bounds of the percentiles can be determined, finding pairs of values (r, s) for which the desired confidence level of 90% is achieved (Table 3). As stated in Table 1, the strictest accuracy requirements established by FCC to be met in 2019 are 50 meters for 67% of the samples and 150 meters for 90% of terminal-based samples. If the data do not meet these criteria, the data set is rejected for not achieving the proper standards. In this case, a data set of location errors obtained using the mobile web application (developed as part of the proposed Information System) under real conditions were tested using the methodology proposed by the FCC for assessment of compliance with accuracy requirements (FCC 2000). To this end, the data tables were sorted in ascending order of accuracy for each observed location error, and for each 100 samples obtained. According to Table 3, the sample must satisfy the following expression:

½ðaccuracy74 < 50 mÞ and ðaccuracy100 < 150 mÞ or

observations made with the Android device, accuracy decreases at 13.26 m. In this case, the value for the Samsung Galaxy S4 device is 2DRMS = 26.52 m. It might be concluded that, at a 95% confidence level, the accuracy obtained with the mobile web application using the GPS of the Samsung Galaxy S4 device falls within a 26.52 m radius from the real location value in an urban environment with demanding conditions for

½ðaccuracy75 < 50 mÞ and ðaccuracy99 < 150 mÞ 5.1. Android device verification First, the verification process was performed with the samples obtained using the mobile web application in an Android device. The results show that:

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Fig. 3. Horizontal error measurements for both devices.

Fig. 4. Control panel - tracking remote workforce.

Accuracy74android ¼ 19:2 m < 50 m and Accuracy100android ¼ 41:1 m < 150 m Accuracy75android ¼ 19:7 m < 50 m and

5.2. iOS device verification A verification process was also performed with the samples obtained using the mobile web application in an iOS device. The results show that:

Accuracy99android ¼ 34:2 m < 150 m Following the FCC’s proposed methodology with samples obtained with the Android device, using the mobile web application of the proposed Information System under real world conditions, and maintaining a 90% confidence level, this study verifies compliance with the strictest requirements established by this organization.

Accuracy74iOS ¼ 13:9 m < 50 m and Accuracy100iOS ¼ 27:5 m < 150 m Accuracy75iOS ¼ 13:9 m < 50 m and Accuracy99iOS ¼ 24:8 m < 150 m

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Table 3 Horizontal error value sample identification for comparison with thresholds of 67% and 95% required by the FCC for a 90% confidence level. Source: Adapted from (FCC 2000). Sample size

Test sample pairs

50 60 70 75 80 85 90 95 100

(x40, (x47, (x53, (x57, (x60, (x64, (x67, (x71, (x74,

y45) y60) y70) y75) y80) or (x63, y79) y85) or (x66, y84) y90) or (x68, y89) y95) or (x72, y94) y100) or (x75, y99)

Following the FCC’s proposed methodology with samples obtained from the iOS device, using the mobile web application of the proposed Information System under real world conditions, with a 90% confidence level, this study verifies compliance with the strictest requirements established by defined by this organization. 5.3. Discussion of results The most important conclusion to be drawn from these results is that an Information System for the behavior-based control of workforce can always identify the employee’s location with an accuracy level that comfortably fulfills current and future peoplelocation requirements of the strictest Emergency Systems, which at present provide the only reliable quantitative reference to validate the quality of people’s geographical location data in this context. This practice also protects the company’s investment in implementing the proposed mobile-based GPS Information System. Due to the evolving nature of technological innovation, mobile GPS receiver sensitivity will increase still further, providing more precise and accurate results than those here obtained (Cao, Wang, & Li, 2003; Elnahas & Adly, 2000; Schiller & Voisard, 2004). Further, both the iOS and the Android devices used in the verification process performed quite well in terms of power consumption throughout working hours without battery recharges. This result is especially important when taking into consideration the battery drain typically associated with the GPS receiver circuit. 6. Conclusions This paper proposes an Information System for the behaviorbased control of workforce. It defines the architecture and functionality, while always complying with business needs concerning control and taking into consideration implementation costs. These results were achieved using open software technologies and adapting these technologies to give adequate support to the BYOD paradigm. A prototype was developed and tested under real world conditions, that is, not by evaluating strictly controlled parameters of a device’s accuracy as in empirical tests but by testing the Information System as a whole under real conditions that reflect normal business activity. Under these conditions, compliance with the strict accuracy demands proposed here as a reference was also verified. This study’s main theoretical contribution is the unification of three concepts that previous studies have only evaluated individually. These are the consumerization of ITs, the real capabilities of personal mobile devices that employees can use in the workplace, and the opportunity that new Mobile Web technology represents

for providing location information independently of the device in use, without problems of app installation, in a model that significantly reduces cost compared to other technology options. Our findings also have theoretical implications for studying the quality of the location data obtained with real devices like the smartphone and their validity for controlling persons. In fact, we propose a specific methodology for analyzing the precision and accuracy of the location mechanisms available in smartphones, thereby complementing previous studies that do not include mobile devices and analyze only topography-quality GPS instrumentation. Another theoretical contribution of this study is its development of a new reference framework for the requirements needed, on both quantitative and qualitative levels, for the accuracy of mobile locations systems used in presence control information systems, particularly those related to control of remote labor force. As to practical implications, this study demonstrates the viability of implementing a Mobile Information System to control remote labor force that permits firms to gain competitiveness while reducing costs and increasing ROI. This goal can be achieved by adopting the BYOD paradigm, which permits employees to use their own smartphone-type devices in the workplace. Another practical implication of this study is its demonstration that the smartphone location services accessible from mobile web applications through the standard W3C API are already fully usable and operational, and that the precision and accuracy of these applications are no different from those used through native app interfaces. We can thus confidently propose the development of universally available location-based devices using only Mobile Web technologies. Among the limitations of this study, we would first indicate that, in analyzing the precision and accuracy of smartphone-type location device services, we only measured two high-end mobile devices (Samsung Galaxy S4 and iPhone 5), which are representative of the operating systems with the greatest market presence (Android and iOS). To ensure completeness, this research should be extended to other systems that are increasing in market share, such as Windows Phone and, due to their interest as presences in medium-to-low-end devices with greater potential for expansion in emerging countries, Firefox OS and Tizen (Courtney, 2013). Further, our study does not take into account issues of security, nor has it analyzed the anti-fraud potential for preventing action to supplant locations, especially for the case of workers who form part of the remote labor force (Shokri, Theodorakopoulos, Papadimitratos & Kazemi, 2013). Future studies could extend the Information System presented here in different directions: First, we propose evaluating options for integrating the Information System with different emerging technologies for location inside buildings, so that firms can obtain more accurate location of their labor force inside their own installations in adverse interior conditions. Other studies could evaluate the integration of mechanisms that facilitate automatic presence control, without requiring the worker’s intervention when he or she enters the firm’s own installations, for example using beacons based on short-range radio-frequency technologies, such as Bluetooth LE. One could also analyze the viability of using non-contact technologies such as NFC to accelerate the process of signing in when the labor force is inside the company’s installations, providing proof of presence using an NFC tag as an additional form of authentication. We also propose investigating the application of antifraud security measures to guarantee that the location data have not been falsified, an especially sensitive topic in the specific case of remote labor force, which due to its unique characteristics may require implementation of more exhaustive control. In this case, we recommend using extra authentication factors that include, among

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