information capability and value creation strategy

Information capability and value creation

strategy: advancing revenue management

through mobile ticketing technologies

Ting Li,Eric van Heck andPeter Vervest

Department of Decision and Information

Sciences, RSM Erasmus University,

The Netherlands

Correspondence: Ting Li, Department ofDecision and Information Sciences, RSMErasmus University, P.O. Box 1738,3000 DR Rotterdam, The Netherlands.Tel: þ31 0 10 408 1961;Fax: þ31 0 10 408 9010;E-mail: [email protected]

Received: 18 February 2008Revised: 1 July 20082nd Revision: 15 October 20083rd Revision: 17 November 20084th Revision: 2 January 2009Accepted: 12 January 2009

AbstractUsing the process-oriented view and resource-based theory, we investigate

how mobile ticketing technologies can successfully enable revenue manage-

ment. We collect data from 17 cases worldwide in which smart cards andmobile devices have been adopted in the public transport industry over the last

decade. The use of these technologies allows service providers to capture real-

time and complete information of customers’ actual travel. This enables service

providers to employ advanced price differentiation and service expansionstrategies and achieve new ‘best practice’ in revenue management. The results

demonstrate that service providers that use more sophisticated mobile

ticketing technologies are more likely to adopt advanced strategies to createvalue. Further, they are more likely to achieve higher performance gains.

European Journal of Information Systems (2009) 18, 38–51. doi:10.1057/ejis.2009.1;

published online 17 February 2009

Keywords: information capability; mobile ticketing; public transport; revenue manage-ment; smart cards; value creation

IntroductionThe past decade has witnessed an increase in the application of revenuemanagement. Firms use various quantitative analysis techniques such ascustomer segmentation and pricing optimization to allocate capacity andmanage demand. The success of firms such as American Airlines (Smithet al., 1992) and National Car Rental (Geraghty & Johnson, 1997) hasencouraged scholars (Talluri & van Ryzin, 2004; Garrow et al., 2007) andpractitioners (Riddell, 2006) to explore the possibilities of leveragingdetailed customer data for revenue management. This process is furtheraccelerated by the increased implementation of advanced informationtechnologies (IT). For example, using mobile ticketing technologiesenabled by smart card and mobile devices, firms can learn about customerbehavior with far more precision. This permits them to adjust their servicesand prices to improve their revenues and operations. Hence, therehas been a growing interest in information systems (IS) research tostudy revenue management supported by mobile ticketing technologies(Elmaghraby & Keskinocak, 2003; Talluri & van Ryzin, 2004).

Recently, strategic pricing decisions in the presence of IT has become anactive area in the IS discipline (Brynjolfsson & Smith, 2000; Clemons et al.,2002; Bergen et al., 2005; Oh & Lucas, 2006; Kauffman & Wood, 2007).Earlier research has provided evidence of the important role that IT playsin supporting pricing-related decisions. On the demand side, IT increasesmarket transparency by lowering customers’ search costs for product

European Journal of Information Systems (2009) 18, 38–51

& 2009 Operational Research Society Ltd. All rights reserved 0960-085X/09

www.palgrave-journals.com/ejis/

and service information (Bakos, 1997). On the supplyside, IT creates opportunities for firms to adjust theirpricing decisions (Bergen et al., 2005; Kauffman & Wood,2007). IT enables companies to set prices with higherprecision, segment customers more accurately, trackcustomer behavior, and adjust prices promptly. Pricingstrategies present a rich opportunity to apply IT and IS tocreate and sustain competitive advantage. Earlier researchsuggests that the increased adoption and development ofdynamic pricing and revenue management can beattributed to the increased availability of demand data,the ease of changing prices due to IT, and the availabilityof decision-support tools that handle large-scale optimi-zation (Elmaghraby & Keskinocak, 2003).

Though the role of IT in revenue management is oftenacknowledged (Kimes, 2001; Elmaghraby & Keskinocak,2003; Talluri & van Ryzin, 2004), we have found limitedsystematic research examining the impacts of the use ofcustomer demand data on the performance of revenuemanagement strategies. Our research addresses this voidby empirically studying the business value of IT inrevenue management. Specifically, we are motivated bythe recent adoption of smart cards and mobile technol-ogies (Turban & Brahm, 2000), and by calls from scholars(Shugan, 2004; Talluri & van Ryzin, 2004; van Ryzin,2005). We explore the following research questions: Whatis the business value of mobile ticketing technology? How andwhy does the improved IT and customer information advancefirms’ revenue management? Consequently, what are theimpacts on firm performance?

Using the process-oriented view, we argue that firmsthat use smart cards and mobile technologies will create ahigher-order process capability (i.e., value creation strat-egy), which then leads to performance gains for them. Inparticular, we suggest that the use of mobile ticketingtechnologies enables firms to benefit from revenuemanagement. We employ a multiple case study approach(Eisenhardt, 1989) and test our arguments through astudy of 17 cases in which mobile ticketing technologieswere implemented over the last decade. The results pro-vide evidence that firms using detailed customer beha-vior information are able to use very advanced pricedifferentiation and service expansion strategies. Further,these firms are most likely to achieve higher performance.

We chose the public transport industry (including bus,tram, metro, and railway) as our research setting for tworeasons. First, the increased adoption of IT, such as smartcards and mobile technologies in the last decade hasallowed public transport operators (PTOs) to exploreopportunities of revenue management that were notpossible earlier. Second, there is a strong need for PTOs toseek for solutions to reduce the concentrations of peaktravel, which causes problems such as over-crowding,dissatisfied customers, low capacity utilization, and lowrevenue.

The remainder of this paper is organized as follows.First, we introduce our main theoretical perspectives.Next, we explain our conceptual model and develop the

propositions. Then, we introduce our research methodol-ogy and construct measurement. Subsequently, wepresent the analysis and results. Finally, we concludewith discussions and directions for future research.

Theoretical perspectiveIn this section, we present and discuss the process-oriented view, resource-based theory, and revenue man-agement literature. These theoretical perspectives help usidentify a basis for formulating our conceptual model andpropositions.

A process-oriented view of business value of ITThe business value of IT has long been a subject forresearch and intensive debate (Brynjolfsson & Hitt, 1996;Dewan & Kraemer, 2000). Using production theory,previous research has demonstrated the payoffs of ITinvestment at the firm level (Brynjolfsson & Hitt, 1996;Gurbaxani et al., 2000; Duliba et al., 2001), the industrylevel (Devaraj & Kohli, 2003), and the economy level(Dewan & Kraemer, 2000). Recent IS studies havereframed the discussion, from the direct performanceimpact of IT investment (Brynjolfsson & Hitt, 1996; Hitt& Brynjolfsson, 1996) to how and why IT shapes thehigher-order process capabilities that create performancegains for firms (Barua et al., 2004). Using the process-oriented view, this stream of literature focuses on theusage and value creation of IT innovations (Zhu &Kraemer, 2005). The process-oriented view suggests thatfirm level impact of IT can only be measured through itsintermediate process contributions (Barua et al., 2004).The argument here is that IT is deployed in support ofspecific activities and purposes, and therefore, the impactof IT should be assessed at the place where the first-ordereffects are expected to be realized.

This approach is also consistent with a second streamof research that takes a contingency approach, suggestingthat the need is to consider other variables that maymediate or moderate firm performance. Firms first focuson their business strategies and then allocate IT resourcesto support their core competencies. IT is viewed as anenabler of specific strategies designed to achieve superiorperformance (Fairbank et al., 2006).

Resource-based theoryStrongly based on the strategic management literature,the resource-based view of the firm posits that firmscompete on the basis of unique corporate resources thatare valuable, rare, difficult to imitate, and non-substitu-table by other resources. In the IS literature, resource-based view has been used to analyze IT capabilitiesand to explain how IT business value resides more inthe organization’s skills to leverage IT in the key acti-vities in a firm’s value chain (Bharadwaj, 2000). Thegreater the use, the more likely the firm is to deve-lop unique capabilities, and the firm’s core IT infra-structure generates higher value (Bharadwaj, 2000; Zhu &Kraemer, 2005). According to Zhu & Kraemer (2005),

Information capability and value creation strategy Ting Li et al 39

European Journal of Information Systems

resource-based theory provides a theoretical basis forlinking IT use and value creation.

Revenue management theoryRevenue management deals with selling the right productto the right customers at the right time for the right priceto maximize firm revenue (Kimes, 2001). There are twomain methods in revenue management: quantity-basedand price-based revenue management (Talluri & vanRyzin, 2004). Quantity-based revenue management fo-cuses on optimal product allocation. Price-based revenuemanagement mainly deals with the demand side of thesupply-demand equation. In the public transport indus-try, the price-based method (i.e., price and servicedifferentiation) is more appropriate. This is because byusing mobile ticketing technologies, PTOs can easily setand adjust prices at minimal costs while at the same timereceiving customers’ instantaneous feedback. Further,Talluri & van Ryzin (2004) suggest that the price-basedmethod is the most preferred approach to revenuemanagement.

Not all firms are able to employ revenue manage-ment strategies in their business. The ones where suchstrategies are possible have the following characteristics.First, on the demand side, the higher the customerheterogeneity, the more potential there is to exploit thisheterogeneity strategically and tactically to improverevenues (Talluri & van Ryzin, 2004). Demand shouldexhibit some kinds of variation, such as variations due toweather, changing patterns on holidays, and time-of-dayor day-of-week. Second, on the supply side, a firm shouldoperate with a relatively fixed and inflexible capacity andproduction constraints. It may not be able to cope withvariations in demand. Further, the products and servicesit offers should be perishable and cannot be held ininventory. Third, in a cost and pricing structure, firms

should have relatively high fixed costs but low marginalcosts of production. Last, a firm should have the capacityto capture abundant customer data via IT. Advancedinfrastructure is needed to collect and store demand dataand automate pricing decisions.

Conceptual model and propositionsUsing the process-oriented view and resource-basedtheory, we now develop a conceptual model to explorethe use and value of mobile ticketing technology indeveloping revenue management strategies. We presentour conceptual model (see Figure 1), explain the keyelements of the model, and propose two propositions.

Information capabilityWe define information capability as a firm’s ability tocapture the complete customer behavior information. Inour research context, customer behavior informationrefers to the customer (who), the ticket type (what), theorigin and destination (where), the departure and arrivaltime (when), and the travel mode (how). This is measuredby the ability of the IT to capture the dimensions andattributes of customer behavior information that be-comes available.

Mobile ticketing refers to the process whereby customersorder, pay for, obtain, and validate tickets using mobiledevices or contactless technologies such as smart cards. Inthe public transport industry today, four types oftechnologies are commonly used: paper tickets, magneticcards, smart cards, and mobile phones. Paper tickets arethe most basic form and are used by a large number ofPTOs. Dating back to 1960, magnetic cards, together withelectronic gates, were introduced to the transportationsystems to provide customer access control. Since 1997,smart cards have become increasingly popular and aregradually replacing magnetic cards. When a customer

Information Capability

Mobile Ticketing Technology

-Smart Card-Mobile Technology

Value Creation Strategy

-Baseline (access control)-Price differentiation-Service expansion

Firm PerformanceP2

Customer Behavior Information

-Who: Customer-What: Ticket type-Where: Origin and destination-When: Departure and arrival time-How: Travel mode

P1

Figure 1 Conceptual model.

Information capability and value creation strategy Ting Li et al40


uses a smart card, either to make a trip or to purchase atravel product, the product details are captured andlinked to the card. If the customer has registered the cardunder his name, all product and trip details will be addedto this individual customer’s record. Mobile technologyis being adopted at an accelerated rate. For example,Tokyo’s ‘Mobile SUICA,’ which includes a RFID chip isembedded into i-mode FeliCa’s mobile handsets. Thedevice was introduced in January 2006 in Tokyo andgained more than 20,000 subscribers within a week(SUICA, 2008).

Rather than IT itself, information has been argued to bethe source of competitive advantage for firms. Despite aconsiderable number of theoretical and empirical workson the role of IT in creating competitive advantage, theliterature has identified a consistent lack of success byfirms in achieving business value through their ITinvestments, and in particular the difficulties in obtain-ing a sustained competitive advantage (Earl, 1989, 1992;Clemons & Row, 1991; Powell & Dent-Micallef, 1997).The notion that IT per se does not generate sustainableperformance advantage has received increasing supportin the IS literature (Earl, 1989, 1992; Clemons et al.,1993). The ‘strategic necessity hypothesis’ (Clemonset al., 1993) argues that firms cannot expect IT to producesustainable advantage because most IT are readily avail-able to all firms – competitors, buyers, suppliers, andpotential new entrants – in competitive markets. IT,hence, becomes a ‘strategic necessity’ but not a source ofcompetitive advantage. The exception is when firms useIT to leverage or exploit firm specific intangibles (Powell& Dent-Micallef, 1997) to obtain sustained profits.

Yet, despite the success in industries such as airlinesand car rentals, the public transport industry facesdifficulties in fully taking advantage of revenue manage-ment. The reasons are twofold. First, PTOs have limitedinformation about their customers’ actual travel beha-vior. Operations of the public transport are largely basedon an open-access system that limits PTOs’ ability toobtain customers’ information. In this situation, PTOsmainly depend on in-vehicle counting and periodicsurvey to obtain customer behavior information. How-ever, these methods are usually expensive, labor-inten-sive, and time-consuming, and hence, customer travelinformation largely remains outdated, inaccurate or evenunknown. Second, partially due to the limited informa-tion, PTOs have limited ability to predict the variabledemand. As opposed to airlines, PTOs do not havereservation systems that allow them to predict customerarrivals. Thus, they are challenged in estimating thedemand variations of their heterogeneous customers.A senior manager, whom we interviewed stated that ‘it isdifficult to implement a profitable operating environ-ment where our entire business strategy is based on an‘open access’ system for flexible traveling, and revenuemanagement is nearly impossible for us.’

This situation has started to change with the increasedimplementation of mobile ticketing technologies in the

public transport industry in the past decade. Withadvanced IT, PTOs are able to learn about their customers’travel behavior in regard to the location to and fromwhich they travel, what time they travel, how frequentlythey travel, and what ticket they purchase, in (nearly)real-time. This permits the PTOs to explore the possibi-lities of developing revenue management strategies thatwere not possible earlier.

Value creation strategyAccording to Porter (1996), a firm’s value creation strategyis defined as a set of value creation activities it carries outin order to create and deliver value. We distinguish threevalue creation strategies that PTOs use: baseline strategy,price differentiation strategy, and service expansionstrategy. This distinction is consistent with the two broadstrategy categories that are discussed in the strategyliterature (Porter, 1980): low cost leadership, which is ourbaseline strategy, and differentiation, which is pricedifferentiation or service expansion strategy.

Baseline strategy refers to basic value creation, whichis the reason why PTOs implement mobile ticketingsystems in the first place. Fare fraud is very costly forPTOs, for example, it has been estimated to costMetropolitan Atlanta Rapid Transit Authority an esti-mated $10 million per year (Donsky, 2006). The primaryreason for most PTOs to adopt mobile ticketing is tocontrol customer access, prevent fare evasion, and reducefraud. Of course, mobile ticketing also provides ease ofuse for customers, improves passenger flows, reducesticket-purchasing queues, and reduces PTOs’ operatingcosts through accelerating ticket purchase and reducingclerical work.

Differentiation strategy includes price differentiationand product/service differentiation. Porter (1980) arguesthat differentiation strategy is an effective approach tocreate and sustain a firm’s competitive advantage. Serviceproviders that use differentiation strategy are able toprovide products and services that customers perceive tobe unique (Soh et al., 2006). An example familiar to mostis airline ticketing. Airlines dynamically vary ticket pricesand associated conditions based on real-time demandand available capacity at any given departure time.

Service expansion strategy is also rooted in the strategyliterature. It resembles the concept of virtual value chainorchestration as discussed by Hinterhuber (2002). Serviceexpansion strategy is a way to create and capture valueby structuring, coordinating, and integrating the activ-ities of previously separate markets. By relating theseactivities effectively to in-house operations, firms areable to develop a network of activities that create newmarkets. Service expansion is useful in this contextbecause electronic ticketing systems can provide micro-payment infrastructures that permit other service provi-ders to adopt them. As a result of this, service providerscan increase their transactional efficiencies and expandtheir services quickly into other industry sectors.



Firm performanceUsing mobile ticketing for value creation leads to changesin PTOs’ cost structure, revenue, and customer volume.What is even more important is the reputation that PTOscreate. Given PTOs’ social responsibility and publicpressure through governmental regulation, PTOs whofail to justify the impacts of pricing strategies will receiveheavy criticism from the public and politicians (Li &Wong, 1994; Link, 2004). Customers may express objec-tions to crowding, unfairness, and fare complexity.Consequently, this will lead to changes in customers’willingness-to-pay. Customers may even shift to othertransport modes. For example, in December 2002,Deutsche Bahn (DB), using revenue management strat-egy, launched a program to reform its fare structure,focusing on the long-distance passenger market (Link,2004). Within half a year after the introduction theprogram failed. This failure, in part, was caused by lowacceptance and widespread criticism of the new pricingstructure. In the words of DB customers: ‘the price changeis opaque and unfriendly,’ ‘the new price scheme isconfusing,’ and ‘the whole fare structure is in need ofsimplification to make it readily comprehensible andusable by staff and passengers.’

Information capability and value creation strategyInformation processing in organizations is generallydefined as the gathering of data, the transformation ofdata into information, and the communication andstorage of information in the organization (Egelhoff,1982). The conceptual underpinning of informationprocessing theory is to enhance the capability to handleinformation flow and thereby reduce uncertainty.Previous research suggests that the most effective orga-nizational strategies are those that recognize an appro-priate fit between an organization’s ability to handleinformation and the amount and type of informationthat is available or required (Tushman & Nadler, 1978;Egelhoff, 1982). Mobile ticketing technology providesPTOs with detailed customer behavior information thatwas only partially attainable through traditional travelsurveys. Using this information, PTOs can derive the costthat customers are willing to pay in different marketsegments. In contrast to time-consuming surveys, mobileticketing technology gives almost instantaneous consu-mer feedback. The improvement in information quantityand information quality significantly reduces demanduncertainty. The information-processing notion of thefirms allows us to hypothesize a relationship between afirm’s information capability and its choice of appro-priate value creation strategy. PTOs, who recognize theopportunities that the improved information capabilityprovides, will align their activities to create value. Thus,we present our first proposition as follows (P1):

Proposition 1: (The Information Capability and ValueCreation Strategy Proposition): Firms

with a higher information capability aremore likely to use an advanced valuecreation strategy (i.e. price differentiationor service expansion) than firms with alower information capability.

Value creation strategy and firm performanceQuantifiable results from revenue management are foundin both management practice (Cross, 1997) and businesssolutions (McCartney, 2000). Bill Brunger, ContinentalAirlines’ pricing guru (McCartney, 2000), pointed outthat ‘revenue management is all of our profit, and more.’Revenue management success stories are encouraging.American Airlines had an estimated benefit of $1.4billion over a period of 3 years and an annual revenuecontribution of over $500 million (Smith et al., 1992).National Car Rental improved revenue by $56 million inthe first year after a successful implementation of arevenue management system (Geraghty & Johnson,1997).

Grounded in the economics literature, price differen-tiation is the most important revenue managementtechnique. It can date back to the concept of pricediscrimination (Pigou, 1932). Price differentiation refersto any pricing policy under which a seller sets differentprices on various units of the same or a similar product. Itextracts a higher price from existing customers whileextending sales to new customers who would otherwisenot be served with uniform pricing. Economic theoryindicates that price differentiation is inherently good forthe profitability of the firm, because it allows the firm tocapture a larger share of the consumer surplus. Theeconomics of revenue management suggests that themore prices are differentiated by a firm the more revenuewill be generated (Talluri & van Ryzin, 2004). Thus, wepresent our second proposition as follows (P2):

Proposition 2: (The Value Creation Strategy and FirmPerformance Proposition): Firms thatuse a more advanced value creation strategy(i.e. price differentiation and service expan-sion) will outperform firms that use abaseline strategy.

Research methodWe employ a multiple case study method (Benbasat et al.,1987; Yin, 2002) to study large scale mobile ticketingsystems that use smart cards in the public transportindustry. Our study is focused on the time periodbetween 1997 and 2006. This research design has severaladvantages. First, the utilization of multiple cases allowsfor cross-case analysis, which significantly improves theinvestigation of the proposed research model (Benbasatet al., 1987). Second, we control for industry variationsrelated to performance by focusing on smart cardadoption in the public transport industry. Smart card



adoption in other industries exhibits different perfor-mance levels, hence controlling for industry is necessary.Third, studying the population in an industrial sector isuseful, given the relatively small numbers of selectedPTOs of this type. Using this research design we do notsample, but study all comparable PTOs in the industry.As such, we study the entire population.

Case selectionContactless smart cards used to transfer electronicpayments have gained widespread implementation inthe public transport industry in the last decade and willbecome increasingly important for banks and retailersalike (Olsen, 2007). We used the ‘List of Smart Card’directory in Wikipedia (2008) to identify relevant cases.We believe this list to be comprehensive and accurate fortwo reasons. First, we have followed smart card develop-ment over the past few years, and all the major initiativesthat we are aware of are included. Second, we usedalternative search methods (e.g., Google searches, andindustry magazine listings) to identify possible missingcases and no additional cases were added.

We used five criteria to select our cases. First, we onlyincluded cases from North America, Asia, and Europe,which cover more than 90% of the world-wide smart cardimplementation. Second, the list we adopted is compre-hensive and includes various types of smart cardimplementations. We only included cases where a smartcard is used for public transport (usually throughdifferent modes of transport) and excluded cases wheresmart card is only used for retail or identificationpurposes. Third, we chose cases where the smart cardwas introduced between 1997 and 2006. The year 1997was when the first smart card – the Octopus card inHong Kong – was introduced. We thus excluded caseswhere we did not have information on the time of

introduction. Fourth, we are interested in the large-scalesmart card implementation in public transport, howevertechnology adoption is an ongoing process and it isdifficult to obtain the total number of cards that are soldand in use. Thus, we used the population of the locationas a proxy for the size of a given smart card implementa-tion. The argument here is that in large metropolitancities public transport is more important than in smallercities, where daily ridership is not so high. Fifth, for somecases, where more than one smart card is used, weeliminated the secondary card of the two. Although thedirectory listed 139 smart card cases worldwide, only 17cases met our criteria: four cases in the United States, 10cases in Asia, and three cases in Europe (see Table 1 for alist of the selected cases).

Data collectionData were collected from various data sources usingdifferent data collection methods with the objective oftriangulation (Eisenhardt, 1989). Data collection wasconducted in two phases. In the first phase, we selectedthree representative cases and collected data throughunstructured and semi-structured interviews, firm archi-val data, public reports, and email exchanges. We did thisat the beginning in order to establish a good under-standing of smart card adoption related to revenuemanagement and pricing decisions. We chose theOctopus card in Hong Kong, the Oyster card in London,and the OV-chipkaart in the Netherlands for threereasons. First, these three cases employ different valuecreation strategies. Octopus uses a service expansionstrategy, Oyster uses price differentiation strategy, andOV-chipkaart uses the baseline strategy. Second, thissample represents different stages of smart card adoption.Octopus was the first and most successful adoption in theworld so far (Chau & Poon, 2003). Oyster reached a high

Table 1 Worldwide large-scale smart card technology adoption (selected cases)

Introduction Place Transportation service provider/issuing authority Name of smart card

1997 Hong Kong Octopus Cards Limited Octopus

1999 Washington DC Washington Metropolitan Area Transit Authority SmarTrip

1999 Shanghai Shanghai Public Transportation Card Co. Shanghai Public Transportation Card

2001 Singapore EZ-Link Private Ltd. EZ-Link

2001 Tokyo JR East and other 5 operators Suica

2001 Guangzhou Yang Cheng Tong Corporation Yang Cheng Tong

2001 Moscow Moscow Metro Transport Card

2002 Taipei Taipei Smart Card Corporation EasyCard

2002 Chicago Chicago Transit Authority Chicago Card

2004 Bangkok Bangkok Metro Bangkok Metro Smart Card

2004 London Transport for London Oyster Card

2004 Seoul Korea Smart Card Co. Ltd. T-money

2004 Shenzhen Shenzhen TransCard Corporation Shenzhen TransCard

2005 Atlanta Metropolitan Atlanta Rapid Transit Authority Breeze Card

2006 Beijing Beijing Municipal Administration and Communications Card Co. Yikatong

2006 Boston Massachusetts Bay Transportation Authority Charlie Card

2006 The Netherlands Trans Link Systems OV-chipkaart



penetration rate within a relatively short period of time.OV-chipkaart is the first nation-wide implementation,though it is still in its early phases of development.Further, these three cases also had large societal impactsand received widespread media attention. In total, weconducted 16 interviews for these three cases (see Table 2).The interviewees are managers in strategy, pricingand revenue management, and business developmentin three geographically different locations. Each step ofthe research process is well documented, which enhancesthe reliability of our approach (Yin, 2002).

In the second phase of the data collection process, wecollected archival data for the remaining cases fromcompany websites, corporate brochures, newspapers, andmagazine reports. We reviewed relevant web pages frompress releases that made reference to any of the 17 cases.We also reviewed news articles relating to the serviceproviders from LexisNexis Academic and some local newssources, such as Boston Globe. Capturing data both fromthe firm as well as from external reports increases validityand reliability in our data collection process.

Constructs and measurementsIn this section, we define the constructs and measure-ments used to operationalize our conceptual model.Table 3 summarizes the description of and coding foreach construct.

Information capability As discussed earlier, the fourtypes of technology commonly used in public transportare paper tickets, magnetic cards, smart cards, and mobiletechnology. First, we analyzed these four types of mobile

ticketing technologies in terms of product characteristics,process characteristics, and usability. Second, on thespectrum of customer behavior information, we analyzedthe data attributes that could be obtained from eachmobile ticketing technology. Depending on the uniquecharacteristics of the type of technology and the numberof data attributes that it captures, we distinguishedbetween high and low levels of information capability.Ten PTOs had high information capability whereas sevenPTOs had relatively low information capability.

Value creation strategy PTOs or issuing authorities ofsmart cards pursue either a baseline strategy (accesscontrol/fare collection) or a differentiation strategy (i.e.,revenue management strategy and service expansion).We examined the purpose of smart card implementationfor each case and determined the construct of valuecreation strategy.

We identified a case as a baseline case if the PTOs orissuing authorities use smart cards primarily for accesscontrol, providing convenience to customers, and redu-cing operational costs, but not for pricing-related strate-gies. We identified the degree of use of pricedifferentiation strategy using the total number of pricingschemes that PTOs employ as a proxy. Price differentia-tion is very challenging to measure because nearly allPTOs use some form of differentiated pricing. Thus, it isdifficult to determine to what extent a PTO uses pricedifferentiation. Based on the price discrimination litera-ture (Pigou, 1932; Png, 1999), we developed a taxonomyto characterize the pricing practices in the publictransport industry (Li et al., 2007). According to thistaxonomy, we coded each pricing scheme that eachPTO uses, and then computed a differentiation score bysumming the value of each pricing scheme that weevaluated. If a PTO actively uses more than four types ofpricing, we coded the case as actively exercising pricedifferentiation. We identified service expansion strategythrough the use of smart cards for multiple purposes,such as retail, library, and identification, rather than onlyfor transportation purposes (including highway toll gates,parking, and ferries). We found that six PTOs use abaseline strategy, six PTOs use price differentiation, andfive PTOs use service expansion.

Firm performance We developed three qualitative indi-cators of firm performance based on the revenue manage-ment and transportation literature (Talluri & van Ryzin,2004). These indicators are growth in revenue andcustomer volume (Weatherford & Bodily, 1992), reputa-tion (Soh et al., 2006), and longevity (Soh et al., 2006). Wecalculated ordinal measures for each performance con-struct between 1997 and 2006, and we constructed aperformance index from the sum of these measures.Growth in revenue and passenger volume measures howmuch new revenue and how many new customers havebeen attracted. Reputation is computed based on thepositive or negative information stated in the press

Table 2 Overview of interviewees

Cases Number of

respondents

Business unit/functions

Octopus 6 General Manager

Marketing Department (department

manager)

Operations Department (department

manager)

Strategy Development Department

(department manager)

Oyster 3 Pricing Department (researcher)

Transport Research (consultants)

OV-chipkaart 7 Business Development Department

(department manager)

Tariff and Pricing (project manager)

Marketing Research and Advice

(senior project leader)

Revenue Management (department

manager)

Independent Research Firms

(independent consultants)



Table 3 Construct, definition, and measurement

Construct Definition Measurement

Information

capability

A firm’s ability to capture the

complete behavior information in

regard to what, where, when, how,and whom of their customers

0 – Low: If less sophisticated information technology is being used and limited

customer behavior information is captured

1 – High: If more sophisticated information technology is being used and(nearly) complete customer behavior information of actual travel is captured in

real-time

Mobile ticketingtechnology

The sophistication of the mobileticketing technologies that are being

used, which is measured as the

unique characteristics of eachtechnology

Product� Usage mechanism (1 – Contactless; 0 – Contact)

� Memory (1 – High; 0 – Low)

� Durability (1 – Durable; 0 – Low, easily damaged)� Data security (1 – High; 0 – Low)

Process

� Obtainment (1 – Internet; 0 – Ticket office)

� Transaction (1 – Can be viewed; 0 – Cannot be viewed)� Replenishment (1 – Can be reloaded; 0 – Cannot be reloaded)

Usability

� Convenience (1 – High; 0 – Low)

� Speed (1 – Fast; 0 – Slow)� Personalization (1 – Yes; 0 – No)

Customer behaviorinformation

The completeness of the customerinformation of actual travel

Number of data attributes that are captured by each mobile ticketingtechnology: for example, the location to and from which the customer travel,

frequency of travel, etc.

Value creationstrategy

Value creation strategy that is used bythe service provider

0 – Baseline: Smart card is primarily used for access control, fare collection, andproviding speedy and convenient services

1 – Price differentiation: If there are more than four types of pricing schemes

used

2 – Service expansion: Besides public transport (including highway toll gates,parking, and ferry), smart card is also widely used for retail, library,

identification, and other purposes

Price differentiation Price differentiation that the service

provider uses

Number of pricing schemes offered to the customers, minimum 0 and

maximum 8.

� Uniform pricing

� Profile-based pricing� Usage-based pricing

� Distance-based pricing

� Time-based pricing

� Route-based pricing� OD-based pricing

� Mode-based pricing

Firm performance The performance impact of theservice provider, in terms of revenue/

customer volume growth, reputation,

and longevity

Sum of coded values for revenue growth/operational excellence, reputation,and longevity:

� Minimum of 0

� Maximum of 6

Revenue/customer

volume

The increase in revenue and/or

customer volume of the service

provider

0 – reduced

1 – no change

2 – improved

Reputation The reputation among customers,

politicians, and general public

0 – negative

1 – neutral

2 – positive

Longevity Number of years since the adoption

of smart card of the service provider

0 – 0–2 years

1 – 3–5 years2 – 6 years or above



articles of the PTOs that we studied. Because PTOs havestrong public roles, they cannot simply focus on revenuemaximization. They need to satisfy customers andpoliticians. Given the same increase in revenue andcustomer volume, a firm with a better reputation may beconsidered to be more successful than those with worsereputations. Longevity is computed from the numberof years since the introduction of the smart card, as statedon the service providers’ website. This measure isconsistent with Soh et al. (2006). We computed an overallperformance index for each PTO by summing the

variables of the three dimensions. The indices show afair degree of variance from 0 to 6. Eight PTOs have ascore between 0 and 3, and seven PTOs have a scorebetween 4 and 6.

Table 4 summarizes the frequency distribution of the17 PTOs by information capability, value creationstrategy, and performance.

Analysis and resultsAs suggested by the literature, we employed nonpara-metric statistics rather than inferential statistics to validate

Table 4 Frequency cross-tabulation

Information capability Performance Value creation strategy

Baseline Price differentiation Service expansion Total

High High 0 4 5 9

Low 1 0 0 1

Low High 0 0 0 0

Low 5 2 0 7

Total 6 6 5 17

Table 5 Mobile ticketing technology comparison

Paper ticket Magnetic card Smart card Mobile

Product view

Usage mechanism Purchase ticket

before/while traveling

Contact: card has to be

inserted into slot

Contactless: card can be

read in proximity

Contactless: card is

embedded into mobile

Memory None Limited High, allow innovative

pricing policy

Very high, allow interaction

with other technology

Durability Low Easily damaged Low Durable (plastic) Durable

Data security Low (lost, stolen) (information lost through

demagnetization)

Medium (encryption, value

could be retrieved if card is

lost)

High (mature security

technology from telecom)

Process view

Obtainment Ticket office

Ticket vending

machine

Ticket office

Ticket vending machine

Internet

Ticket office

Ticket vending machine

Internet

Internet

Transaction None Transactions can not be

viewed

Transactions can be viewed

online

Transactions can be viewed

on the mobile or online

Payment can be incorporated

into one single mobile bill

Replenishment None Card can not be reloaded Card can be reloaded online

Options are also available for

automatic replenishment

Automatic replenishment

Usability

Convenience Low (cumbersome

cash handling,

requires exact

change)

Medium High (avoid ticket

purchasing)

Very high (no additional card

needed)

Speed (boarding

time)

Slow Slow Fast, speed up journey Fast, speed up journey

Personalization

possibility

No No Yes Yes



the propositions (Soh et al., 2006). Nonparametricmethods are preferable for three reasons. First, althoughthe number of selected cases is relatively small, we studythe whole population of the large-scale smart cardadoptions in the public transport industry and thus donot need to make assumptions relating to the populationdistribution. Therefore, the distribution-free nature of thenonparametric method is more appropriate for theanalysis of the whole population than the small samplesize. Second, the ordinal scale of our construct measure-ment calls for the use of a nonparametric method, whichyields higher power than the corresponding parametrictests. Third, rank-based nonparametric statistical tests arenot affected by outliers (Hollander & Wolfe, 1999), andhence are more suitable for the analysis of PTOs, whereoutliers are common. For example, Octopus is a clearoutlier based on adoption rates and transaction volumes.

Information capabilityTo operationalize the conceptual model, we first lookedat different types of mobile ticketing technologies usedby PTOs. We then analyzed different data attributes ofcustomer behavior information that could be obtainedthrough mobile ticketing systems. We summarized thedifferences among paper tickets, magnetic cards, smartcards, and mobile technologies based on the uniquecharacteristics of product, process, and usability (seeTable 5). Next, we examined the different data attributesobtained by each mobile ticketing technology. Wecategorized them into different information dimensionsincluding service, purchasing, personal, temporal, andspatial. We found that paper tickets include the mostbasic information on buying dimension (i.e., travelproduct purchase time/date, location, and price) andservice dimension (i.e., travel mode and vehicle type).Additionally, magnetic cards can capture temporal di-mension information (i.e., time and date of departure).Furthermore, smart cards add a detailed personal dimen-sion (i.e., name, age, gender, address, and profession),whereas mobile technology includes full spatial dimen-sional information (i.e., route and origin/destination)and permits PTOs to easily and precisely capture the fullroute and complete information of customer travel in theentire transportation networks.

Information capability and value creation strategyOur validation of proposition 1 suggests that IT thatprovides effective customer information allows PTOs todevelop advanced value creation strategies (i.e., pricedifferentiation and service expansion). Table 6 showsthat nine out of 10 PTOs that have high informationcapability implemented revenue management strategywith price differentiation or service expansion. Bycontrast, five out of seven PTOs that have low informa-tion capability used a baseline strategy. We used theMann-Whitney U test to examine the differences in valuecreation strategy between high and low informationcapability. We tested against the null hypothesis of equalvalue creation strategy for both high and low informa-tion capability. We concluded that value creation strategyis significantly different across the information capability(P¼ 0.006). Table 7 summarizes the results of ournonparametric tests.

Value creation strategy and firm performanceOur validation of proposition 2 suggests that serviceproviders are more likely to succeed with a value creationstrategy of price differentiation or service expansion.Table 8 shows that all high performance PTOs implementeither price differentiation or service expansion. The factthat two PTOs that use price differentiation are also lowperformers is not inconsistent with our argument. A goodstrategy does not guarantee success – many other factorsinfluence success. In contrast, none of the baseline PTOsexhibited high performance. We validated proposition 2using the same procedure used to validate proposition 1.

Table 6 Value creation strategy and informationcapability crosstabs

Information capability Total

Low High

Value creation strategy

Access control 5 1 6

Price differentiation 2 4 6

Service expansion 0 5 5

Total 7 10 17

Table 7 Validation of proposition 1

Value creation strategy Information capability

High Low

Mean rank 11.65 5.21

Sum of ranks 116.50 36.50

Count 10 7

Mann-Whitney U 8.50

Wilcoxon W 36.50

Test P-value** 0.006*

* Po0.01 (Higher rank indicates higher levels of value creation strategy).** Significant level.

Table 8 Firm performance and value creation strategycrosstabs

Value creation strategy Total

Baseline Price differentiation Service expansion

Firm performance

Low 6 2 0 8

High 0 4 5 9

Total 6 6 5 17



The main difference here is that we tested firm perfor-mance across the three value creation strategies. Weconcluded that PTOs that use price differentiation andservice expansion outperform those with baseline strate-gies (P¼0.019 and 0.002). Table 9 summarizes the resultsof the pair-wise comparison using Mann-Whitney tests.

Findings and discussion

Major findings and interpretationsThe empirical validation of our propositions leads to twomajor findings.

Finding 1: Service providers that use more sophisti-cated mobile ticketing technologies (such as smart cardand mobile technology) and have real-time and completeinformation on customers’ actual travel, are more likelyto adopt price differentiation and service expansionstrategy.

From a product, process, and usability viewpoint, weexamined the unique characteristics of three commonly-used mobile ticketing technologies and compared themto paper tickets. Combined with the analysis of the dataattributes captured by each technology, we observeddifferent levels of information capability among theselected cases. When a ticketing system is implementedby a PTO, the first goal is to reduce fare evasion andachieve operational efficiency. When PTOs start usingmore advanced mobile ticketing technologies such assmart cards or mobile devices, they are soon able toobtain more detailed individual customer behaviorinformation. This information allows them to employprice differentiation strategies. Further, because smartcards and mobile devices move customers quickly throughthe payment process, they are particularly attractive toretail segments where speed and convenience of paymentare essential. The technology adoption in the publictransport industry creates a large customer installed base;this makes it easier for the service providers to expand intoother markets. The empirical results provide strongsupport for proposition 1. It suggests that service providersthat have a higher information capability are more likelyto use price differentiation and service expansion strate-gies, compared to the ones that have a lower informationcapability.

Finding 2: Service providers that adopt advancedmobile ticketing technologies and employ price

differentiation and/or service expansion strategies aremore likely to have higher performance gains comparedto the ones that use only the baseline strategy.

As theorized in the revenue management literature(Talluri & van Ryzin, 2004), the service providers that useprice differentiation tend to achieve higher performance.Although ticketing systems are often seen as expensiveinvestments in infrastructure, they can improve PTOs’access control and enhance their operational efficiency.Further, the systems also provide improved informationon customer behavior, which creates an opportunity tooptimize and individualize their service offerings. Im-proved products and service offerings can justify theinvestment premium. Our results suggest that the serviceproviders that use price differentiation and serviceexpansion strategy have a greater chance to be successful.

ImplicationsThe findings of our study have several implications forresearchers and managers. For researchers, our studyproposed and empirically tested a model that encom-passes information capability, value creation strategy, andfirm performance. Past research has suggested that actualusage may be an important link to IT value (Devaraj &Kohli, 2003). However, this link has been missing in theliterature and much of the work has typically focused on‘adoption vs non-adoption’ (Zhu & Kraemer, 2005; Zhuet al., 2006). Our model moves beyond the adoptionphase and accounts for the actual usage as a critical stageof value creation. As a result, we are able to betterunderstand the post-adoption variations of mobile tick-eting technology.

Further, in contrast to prior studies that have largelyfocused on revenue management practice in the airlineindustry, our study sheds light on the less understoodpossibilities of revenue management in the publictransport industry. Previously, PTOs had limited informa-tion about their customers’ actual travel behavior andlimited ability to predict variable demand. As a result,revenue management was considered ‘nearly impossible’for them. Our study examined the usage of mobileticketing technologies and explained how PTOs canleverage these technologies to enable and advance theirrevenue management practices.

For managers of firms in the public transport industry,the results underline the value of understanding how

Table 9 Validation of proposition 2

Firm performance Value creation strategy Value creation strategy Value creation strategy

Baseline Price differentiation Baseline Service expansion Price differentiation Service expansion

Mean rank 4.50 8.50 3.50 9.00 5.17 7.00

Sum of ranks 27.00 51.00 21.00 45.00 31.00 35.00

Count 6 6 6 5 6 6

Mann-Whitney U 6.00 0.00 10.00

Wilcoxon W 27.00 21.00 31.00

Test P-value 0.019* 0.002* 1.174



PTOs create value through the use of customer behavioralinformation. PTOs who seek and actively engage inexploring their information capability and employingprice differentiation and service expansion strategies aremore likely to succeed compared to the ones who onlyuse the baseline strategy. This difference in performancehas important implications for revenue models, pricingstructure, and the overall service operation strategies forPTOs. More importantly, PTOs who can make better useof customers’ travel behavior information can adjusttheir products and services quickly and effectively, andimprove their revenue and service operations.

Further, it is important for PTOs to recognize theimportant role that customers play in their serviceoperations. The public transport industry is (partially)subsidized by government and has very strong socialresponsibilities. The primary reasons for government topay subsidies are to provide transport services to thepublic, alleviate congestion, reduce pollution, and pro-mote economic growth. Thus, there is a limit to how farPTOs can practise revenue management. Instead of usinga profit-maximization approach as many other industriesdo, PTOs are more likely to benefit from using acustomer-focused approach. On the one hand, they needto pay special attention to the effects of price increaseand tariff structure adjustments, and the impacts of seatavailability and service punctuality, which might lead tocrowding and discomfort. On the other hand, PTOs canbenefit from engaging in activities that improve custo-mer satisfaction, for example designing and deliveringvalue added services to meet customer needs, such as real-time travel information.

Future research and conclusionThis study makes several contributions to the IS literatureby examining the use of modern IT in the developmentof revenue management. Nevertheless, the findingsshould be evaluated in light of the limitations. First, themeasures of revenue and customer volume as well asreputation for performance impacts were subjective inthe sense that we relied on the available data from pressreleases and news articles that were read by the authors.While we have been careful in assessing the potentialbiases inherently associated with such data, it would havebeen desirable to have more objective measures of perfor-mance. Second, this study does not distinguish betweenthe operating environments of the service providers. Itcould be that some service providers have more com-mercial freedom compared to others, and this would leadto a different (non)-profit-maximization agenda andoperational boundaries. Further, we find that someperformance effects cannot be explained by the choiceof value creation strategy. Some choices of value creationstrategy cannot be explained by the change in informa-tion capability. They may be driven by other competitiveconsiderations, including organizational capabilities,sophistication of competition, a firm’s chosen price,

and service profile, among other considerations. Theselimitations suggest avenues for further research: we offersome specific suggestions. The measures of key variablessuch as firm performance supplemented by objectiveperformance data could be much refined in the futureresearch by controlling for the characteristics andcommercial objectives of the service providers. Futureresearch can conduct more in-depth interviews to findout the evolution of smart card usage and value.

This study was motivated by the process-oriented viewof the business value of IT. It is grounded in the revenuemanagement literature and resource-based theory. It hastheoretically developed and empirically evaluated aresearch model that examines the use and impact ofmobile ticketing technology and improved customerbehavior information at the firm level. Using multiplecases, this study investigates the value creation process ofmobile ticketing technologies and their enablement torevenue management strategies.

This study shows that mobile ticketing technologieshave unique product, process, and usability character-istics compared with the traditional ticketing channel.These technologies increase firms’ information capabilityin terms of both information quantity and informationquality. It finds evidence that firms create value throughthe use of mobile ticketing technologies in three ways.First, benefiting from the installed electronic gatinginfrastructure, mobile ticketing technologies reduce fareevasion, offer customer convenience, and reduce opera-tional costs. Second, the technologies enable firms tocollect more detailed customer information, whichincreases firms’ abilities to design price and servicedifferentiation strategies to create value. Third, mobileticketing systems provide micro-payment infrastructuresthat permit other service providers to adopt them. As aresult of this, service providers can increase theirtransactional efficiencies and expand their servicesquickly into other industry sectors.

This study finds that service providers using moresophisticated mobile ticketing technologies and havereal-time and complete information on customers’ actualtravel will also use price differentiation and serviceexpansion strategy. Moreover, these providers haverelatively higher performance gains. Although we usedthe public transport industry as our research context, weacknowledge that in an exploratory sense, this studyindicates a potential model applicable across domainsand which can be applied to companies that areexamining modern technologies to develop revenuemanagement strategies.

AcknowledgementsThe authors thank the anonymous reviewers and associate

editor of this journal and the conference participants of the

Academy of Management Meeting 2008 for their helpfulcomments. The authors gratefully acknowledge support

from Erasmus Research Institute of Management.



About the authors

Ting Li is an assistant professor of Decision andInformation Sciences at Rotterdam School of Manage-ment Erasmus University, where she also receivedher Ph.D. Her main research interests include thestrategic use of information technology, competitivestrategy and economics of information systems, pricingand revenue management, and business networks.E-mail: [email protected] van Heck is a professor of information managementand markets at the Department of Decision and Informa-tion Sciences of RSM Erasmus University and director ofdoctoral education at Erasmus Research Institute of

Management (ERIM) in Rotterdam. He conducts researchand teaches on the strategic and operational useof information technologies for companies and markets.E-mail: [email protected] Vervest is a professor of business networks at theRotterdam School of Management, Erasmus University,and partner of D-Age, corporate counsellors and invest-ment managers for digital age companies (London –Amersfoort – Sunnyvale). His specific field of researchconcerns the development and application of enablingtechnologies for smart business networks. E-mail: [email protected]

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