Obsolescence risk in advanced technologies for retailing: A management perspective

Journal of Retailing and Consumer Services 20 (2013) 225–233

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Obsolescence risk in advanced technologies for retailing: A management perspective Eleonora Pantano n, Gianpaolo Iazzolino 1, Giuseppe Migliano 1 Department of Mechanical, Energy and Management Engineering, University of Calabria, Via P. Bucci, cubo 46C 87036 Arcavacata di Rende CS, Italy

a r t i c l e i n f o

abstract

Article history: Received 31 July 2012 Received in revised form 3 December 2012 Accepted 2 January 2013 Available online 1 February 2013

In recent years, a great deal of research focused on the introduction of advanced technologies for making traditional stores more appealing and attractive, with several benefits for the retail process. Since the introduction of these innovative systems involves several risks that can have a negative impact on business profitability, this paper aims at investigating to what extent it is possible to reduce these risks by proposing an explorative framework for a successful risk management strategies in retail context. Key results of this research concern the importance of the risk management also for retail sector, with emphasis on the introduction/adoption decision of innovative technologies in the points of sale, with consequences for retail-oriented industries. To achieve this task, the current study synthesizes findings from several fields such as management, marketing, and computer science. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Risk management Retailing Technology management Technology obsolescence risk Innovation management

1. Introduction Capacity of innovating has become a critical factor for firms and organizations in order to improve competitiveness, sales growth, efficiency and productivity (Guan et al., 2006). Hence, innovation capability of the firms needs to be improved in order to adopt new advanced systems more quickly than competitors (Wang et al., 2008). For this reason, the global pressure has increased the competitive spirit of enterprises through innovating, and it has reduced the lifecycle of new technologies. Abernathy (1985) suggested describing innovations on the basis of the impact of these on the firm’s existing technological and market/ business knowledge. Hence, these innovations can be incremental or radical (disruptive) according to the importance of the caused changes (Marquis, 1969; Damanpour and Wischnevsky, 2006; Sen and Ghandforoush, 2011). Radical innovations are new functionalities or new technologies that have not been previously identified and they emerge from a discontinuous process, whereas incremental innovations or adaptations are an improvement of existing functionalities by reducing cost, improving efficiency, etc. (Sen and Ghandforoush, 2011). Due to the advantages of innovating and the consumers’ expectations of novel technologies for improving their shopping experience (Pantano and Laria, 2012), also retailing may take advantages by n

Corresponding author. Tel.: þ39 0984492235; fax: þ39 492277. E-mail addresses: [email protected], [email protected] (E. Pantano), [email protected] (G. Iazzolino), [email protected] (G. Migliano). 1 Tel.: þ39 0984492235; fax: þ39 492277. 0969-6989/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jretconser.2013.01.002

adopting technology-based innovations for the points of sale. In fact, this sector can exploit the current advances in technology for making the points of sales more efficient and appealing by introducing innovative and interactive systems (Pantano and Laria, 2012; Pantano and Servidio, 2012; Breugelmans and Campo, 2011). These innovations provide benefits for both consumers by supporting the decision-making process and retailers by providing updated information on clients behaviours and market trends (Sorescu et al., 2011, Vieira, 2010; Shankar et al., 2011). On the one hand, these technologies allow consumers to (i) achieve information and customized contents on favourite products, services, sales, promotions, etc., (ii) compare and choose among alternatives, (iii) search for items, and (iv) calculate total purchases, by providing more convenient experiences in terms of time saving and providing entertainment (Hsiao, 2009; Yoon and Kim, 2007; Bharadwaj et al., 2009). On the other, these technologies provide constantly updated information on market segments, preferences, needs, while shopping, etc., which can be exploited for the development of more efficient (direct) marketing strategies (Pantano and Laria, 2012). Hence, they are able to improve the traditional points of sale by enriching the provided information through the most recent advances in 3D graphics, as well as to provide retailers with information on consumers’ in-store behaviour. To date, the most powerful innovative technologies are RFID (Radio Frequency IDentification) systems (reader and writer for providing additional information on products), storefront displays enriched with virtual reality elements (i.e. virtual mannequins), smart shopping trolleys capable of supporting consumers during the in-store experience, and recommendation systems for mobiles

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(Kowatsch and Maass, 2010; Reitberger et al., 2009; Breugelmans and Campo, 2011). Since the introduction of these technologies dramatically changes the retail process, in terms of store atmosphere, client– vendor relationship, quality of service, and consumers’ shopping experience, several risks can be encountered with possible negative consequences on business profitability, concerning the consumers’ acceptance and effective usage, the monetary investment and late returns on investment, the risk of frequent physical damages and obsolescence of the technical components, etc. Hence, the aim of this paper is to investigate to what extent it is possible to reduce these risks by proposing a new framework for technology risk management in retail context. The study provides an explorative and valuable analysis of obsolescence risks concerning the introduction of a new technology in the points of sale, by highlighting useful tools for scholars and practitioners for better understanding the critical role of risk management for introducing effective innovations in the points of sale. In particular, the first part of the paper investigates risk analysis with emphasis on the obsolescence from a management perspective; whereas the second focuses on the major risk issues involved in most current innovations to figure out the new framework for an efficient risk evaluation for innovations adoption in the retail context. Key results of this research concern the importance of risk management regarding the introduction of innovative technologies in retailing, with consequences for retail-oriented industries, by synthesizing findings from different fields such as management, marketing, and computer science.

2. Theoretical background 2.1. Obsolescence risk management Despite the benefits generated by technological innovations, innovation process is characterized by uncertainty, which affects also the processes that lead to the technological innovations, by making innovating a complex process difficult to assess accurately (Wang et al., 2008; Alkemade and Suurs, 2012). Since rapid advances in technology imply huge investments and late possibility of returns on investments, the adoption of innovations is strictly linked to the uncertainty concerning both the nature of technological changes and the threats of further technological developments able to reduce dramatically the technology life-cycle (Fanelli and Maddalena, 2012; Hekkert et al., 2007). Similarly, uncertainty concerns the occurrence of risks that are unpredictable events able to affect firm’s objectives (ANSI/PMI, 2008). In this scenario, an efficient risk management may lead to several organizational benefits including the identification of more favourable alternatives, by increasing the confidence for achieving the objectives, the chance of developing more successful strategies, the reduction of unexpected threats, risks and problems, and the more detailed estimations, with the subsequent reduction of uncertainty (Ward and Chapman, 2004). In fact, the new management strategies are devoted to reduce the risks prompted by the dynamic forces that are rapidly modifying the competitive environment such as the frequent advances in technology, the rapid growth and diffusion of technology among consumers and competitors, the increasing number of alternatives available for consumers, the increasing effort in innovation, and the increasing the reliance of providers of innovation. In particular, efficient risk management strategies allow firms to (i) recognize potential threats of the market, (ii) identify the main consequences on resources and business profitability, and (iii) modify the subsequent behaviour (McGaughey et al., 1994; Alhawari et al.,

2011). Since the business competitive battle with competitors is largely influenced by the firm ability to predict future actions and establish the best strategy to challenge ever-changing circumstances (Verona and Ravasi, 2003), efficient risk management strategies are compulsory for achieving a sustainable competitive leadership position (Holzmann and Spiegler, 2010). In particular, the risk management process consists of three main phases: (i) risk identification, (ii) risk estimation, and (iii) risk evaluation (Rowe, 1977; Charette, 1990). Concerning risk identification, this phase includes the definition of threats occurring in a particular scenario; the risk estimation phase reduces uncertainly involved in business activities by evaluating the consequences and impact of a certain risk; whereas the last phase involves actions able to reduce risk and increase risk acceptance. Although a detailed and comprehensive list of risks is hard to understand and manage, there are some tools for supporting managers’ decision-making (Hillson, 2002). For instance, the risk identification phase may exploit few fundamental practices such as Risk Breakdown Structure (RBS), which emerges as one of the most useful tools for effective risk identification. It is a hierarchical structure that splits potential source of risks into layers of increasing detail and describes sources of risks in a certain context and details each risk starting from a root node representing a general risk source to deeply understand the source in depth, thus it can also be exploited for risk assessment (Hillson, 2002, 2003). While the risk estimation phase may involve analysis tools such as probability–impact grid analysis. This represents the risks estimation through the attribution of ‘‘high’’, ‘‘moderate’’ and ‘‘low’’ rate. It consists of the definition of a grid with two dimensions: probability of occurrence of event (in columns) and impact of risks on the object of evaluation (in rows). The risk estimated value is calculated as follows (Ward, 1999): rating ¼ probability  impact in fact, it is possible to classify the different kind of encountered risks and rank them through multicriteria methodologies (Iazzolino et al., 2012) for reducing their negative effect on firm performance. Owing to this evaluation, the organization is able to adapt its behaviour in order to perform actions for mitigating the encountered risks, with emphasis on the risks with the ‘‘high’’ rating. In fact, the risks characterized by this rate have the strongest impact on the firm profitability and thus, they require more attention by the managers. Hence, risk management strategies allow understanding and reducing the critical issues emerging from the introduction of a new technology, such as the introduction of Enterprise Resource Planning (ERP) systems (Aloini et al., 2007), with benefits for the financial activities (i.e. bank choice of risk management system) (Danielson et al., 2002; Iazzolino and Fortino, 2012). Furthermore, the risk management is strictly linked to the ‘‘life’’ of a certain technology, by managing the investment decisions and the threats involved in each phase of the ‘‘life’’. In fact, the technological life-cycle (TLC) illustrates the evolution of technical and market characteristics (such as changes in sales) (Anderson and Tushman, 1990; Solomon et al., 2000; Narayanan, 2000). It consists of five main phases: introduction, growth, maturity, decline and phase-out. While each phase has peculiar characteristics, not each technology follows all phases (Solomon et al., 2000). In fact, few technologies may have a false start and die out. The main causes of a false start can be the sudden introduction of a superior competing technology, the improvement of a competing technology, the identification of a problem associated with the technology, failure in achieving the critical mass that enables economies of scale to be achieved, and the lack of a unique and compelling application for the technology (Solomon et al., 2000). After the decline phase, the phase out represents the death of a certain technology, implying that

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it has become obsolete. For this reason, some authors hypothesized also another phase that represents the obsolescence of the system (Concho et al., 2011). Hence, when the technology becomes out of use or out of date; the first is linked to the loss of technology utility value relative to customer expectation, whereas the latter is linked to the presence of technology on the market from a long lasting period (Rai and Terpenny, 2008). Hence, the obsolescence-based risk management (shortly obsolescence risk management) allows assessing proactively and cost-efficiently the risks that are linked to technological obsolescence (Romero Rojo et al., 2012; Gravier and Swartz, 2009). While the out of use might be measured by the users’ acceptance level or a certain technology, by taking into account the number of potential users. To achieve this task, Technology Acceptance Model (Davis, 1989) has been largely exploited. Although it has been initially employed for predicting Internet adoption, it is currently applied to several fields by mainly focusing on the constructs of perceived ease of use, perceived usefulness, attitude and behavioural intention. In fact, it is currently involved in the retail sector for investigating consumers’ attitude towards a new system, their behavioural intention and the subsequent actual usage (Kowatsch and Maass, 2010; Pantano and Servidio, 2012). In particular, Attitude represents user’s assessment toward the technology, whereas the behavioural intention represents the degree to which the user is willing to perform certain behaviour (in this case to use the technology) (Pantano and Servidio, 2012). Concerning the out of date, technologies have internal technical components, and obsolescence of these has a significant impact on the whole technology (Feldman and Sandborn, 2007). Hence, obsolescence can be determined by both the software and hardware part. Its analysis allows forecasting lifecycles based on market and technological factors, by predicting both lifecycle curves and time to obsolescence across the lifecycle curves of components and related technological attributes. Although risk management has been introduced also in the retail context, risk evaluation mainly focuses on the price formation according to actual demand (Ferrer et al., 2011), whereas little attention is paid to the risks involved in the introduction and adoption of innovations in the stores with emphasis on the importance of obsolescence risk concerning the novel technical solutions.

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of-mouth communication) (Di Pietro and Pantano, 2012). Especially two variables try to take into account the risks emerging from the innovation usage, privacy and security, even if these variables only focus on the consumer’s point of view. In fact, the first one involves the interest towards keeping private the personal data during the technology usage (Tsai and Yeh, 2010), whereas the second one is the degree to which a consumer believes that using that technology will be secure, with emphasis on the online environment (Taylor and Strutton, 2010). Hence, the TAM usage replies only to the question if consumer will use the technology without taking into account the effective diffusion. Hence the clients’ intention to use a new technology is not enough for predicting the success of a technology, which could be affected by the introduction of substitute technologies and subject to the wear and tear of its components. Despite the valuation of technology acceptance into the introduction of new technologies in stores, the ‘‘out of date’’ concerning risks, which are related to physical, technological, and functional characteristics, have not been fully exploited in this context and require further investigations.

3. Methodology of research The current work is based on an explorative study design in order to figure out a new framework for successful identification and estimation of possible risks encountered while introducing technological innovations in the point of sales. As a consequence, the purpose is to propose a direction for supporting retailers in understanding, anticipating and reducing the encountered risks. To achieve this task, the present study focuses on the analysis of the possible obsolescence risks faced during the introduction of a new technology based on the immersive store, which has not yet introduced in the stores, even if preliminary study on the prototype showed the large consumers’ interest toward this innovative system (Pantano and Laria, 2012; Pantano and Servidio, 2012). Since each risk can be analyzed through the three main phases of Risk Management (risk identification, risk estimation and risk evaluation), also the obsolescence one would be based on these three phases, even if our study is limited to the first two phases: risk identification and risk estimation, whereas the third one (risk evaluation) can be object of further detailed studies.

2.2. Risk management in retailing The extant literature on risk management strategies on retailing mainly focused on the risk aspects that are related to a certain market (i.e. the energy market). In fact, previous studies have defined risk management either as a set of practices and tools for evaluating, measuring and managing the market risks in a retailer’s portfolio of contracts and plan (Boroumand and Zachmann, 2012), or as a set of services to provide price stability for analysing the volatile price. Other studies focusing on the introduction of innovations in the points of sale, mainly investigated the risk of users’ acceptance of the novel technology, by employing the TAM. In this way, past researches tried to predict consumers’ behaviour towards electronic payment modalities (Schierz et al., 2010), self-service systems (Weng et al., 2012; Eastlick et al., 2012), RFID (Muller-Seitz et al., 2009), and 3D virtual reality (immersive environments) (Pantano and Servidio, 2012). Further variables have been also added to the traditional ones hypothesized by Davis (ease of use, perceived usefulness, attitude, and behavioural intention) in order to develop a more efficient predictive tool, such as perceived enjoyment (the degree to which consumer perceives a certain technology as pleasant), and social pressure (the influence of others, such as friends’ word-

(1) Risk identification The first phase includes the identification of real and potential threats that might affect obsolescence risk. The main tool that allows identifying and categorizing the risks is the RBS (Risk Breakdown Structure) (Hillson, 2002). The RBS structure starts from a root node that identifies the technology obsolescence risk; the second level of RBS identifies the two main categories of risks in which the obsolescence risk has been split out of use and out of date risks. The third level of the structure includes the sub-risks related to the categories previously mentioned (Fig. 1). In particular, the out of use category of risks contains risks which are related to the acceptance of technology; whereas the out of date risks category includes risks related to the physical components of technology and thus, to the TLC (technology life-cycle) of these ones. (2) Risk Estimation Risk Estimation phase aims at evaluating the frequency (or probability of an event) and the impact that might affect the specific risk as the obsolescence one. To achieve this task, the probability–impact grid (or PI grid) (Ward, 1999) has been

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Fig. 1. RBS structure.

employed. This tool allows rating the risks that have been identified in risk identification step both in terms of frequency (probability of occurrence of a threat) and impact that the single risk have on the obsolescence one. Thus, PI grid is characterized by two main axes that are identified by frequency (or probability) on horizontal axis and impact (the ‘‘weight’’ of a certain specific risk on technology obsolescence) on vertical axis. Each axis is divided into three ranges: high, moderate, and low; while the rate that can be calculated as follows (Ward, 1999): Rate of risk ¼ probability  impact PI grid allows identifying categories of risks that could be high, moderate or low risks (Fig. 2). In particular, high probability–high impact, high probability–moderate impact, and moderate probability–high impact characterize the high risks; high probability–low impact, moderate probability–moderate impact, and low probability–high impact characterize the moderate risks; whereas moderate probability–low impact, low probability– moderate impact, and low probability–low impact characterize the low risks. Each quadrant of PI grid is further characterized by a certain score to better understand the value (impact) of each risk (Ward, 1999).

Fig. 2. PI grid with score (adapted from Ward (1999)).

the use of glasses with polarized lens, and browse the environment via a special data glove (Fig. 3). 4.2. Major risk issues

4. The case study of immersive store 4.1. Immersive stores This study is based on the applications of methods for evaluating obsolescence risk to retailing, by investigating the case of immersive stores. These are innovative points of sales extensive employing immersive technologies and recent advances in 3D graphics (Pantano and Laria, 2012; Pantano and Servidio, 2012). In particular, our system is based on the stereoscopic technology. It consists of a wide screen connected to a computer and two projectors that display the same object from two different points of view to create the 3D vision of the item. Consumer can visualize the 3D scenario through

(i) Risk Identification Our analysis starts from the Risk Identification phase, by employing the RBS in order to identify various risks that affect the Obsolescence risk (risk 1) of the Immersive Store (Fig. 3). This risk is characterized by two main categories of risks that are out of use (1.1) and out of date (1.2), as emerged from the literature review analysis. Figs. 4 and 5. Out of use risks (1.1) can be characterized by six main risks emerging from the Technology Acceptance Model of immersive store (Pantano and Servidio, 2012; Pantano and Laria, 2012): perceived difficult of use (1.1.1) (the degree to which a person believes that using the immersive store will be difficult), risk of uselessness (1.1.2) (the degree to which a

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person believes that using the immersive store will be not useful for their shopping activity), risk of boring experience (1.1.3) (the degree to which a person believes that using the immersive store will be not enjoying or pleasant), risk of social pressure in not using (1.1.4) (the degree to which people are pushed by others to not using the immersive stores), privacy risks (1.1.5) (the degree to which a person believes that using immersive store could have negative consequences for own privacy), security risks (1.1.6) (the degree to which a person believes that the immersive store will be not secured during the usage). Concerning the out of date (1.2), the risks included in this category are related to the technical components that are subjected to the internal component obsolescence (physical

Fig. 3. Consumer exploration of the immersive store.

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components). In order to identify these risks, we have carried out an observation of potential users interacting with the system (from March 2010 to December 2011) for obtaining the historical data on each component that compose the immersive store. From the observation, the following risks emerged: obsolescence of computer (1.2.1), obsolescence of projector (1.2.2), obsolescence of screen (1.2.3), obsolescence of data glove (1.2.4) and obsolescence of glasses (1.2.5). These risks have been decomposed in a further level of RBS for major details. Obsolescence of computer (1.2.1) arises when the physical components of computer become obsolete, thus it includes the risk of slowness computer (1.2.1.1), lack of new functionalities emerging from new software tools (1.2.1.2), and limited computer memory capacity (1.2.1.3). Obsolescence of the projector (1.2.2) includes the risks retaliated to the improvements of resolution in new projectors that appear in the market (1.2.2.1), to the improvements of brightness (1.2.2.2),

Fig. 5. Probability-impact grid for immersive store risks.

Fig. 4. RBS for immersive stores obsolescence risk.

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to the improvements colour contrast (1.2.2.3). Obsolescence of the screen (1.2.3) is characterized by the possible substitution by other more efficient monitors (i.e. touch screen monitor, 3D monitor without glasses) (1.2.3.1.) or physical damages caused by users (touches or impacts which cause the phase shift of monitor focus) (1.2.3.2). Obsolescence of the data glove (1.2.4) includes the risk of substitution by other input devices (i.e. motion capture) (1.2.4.1), risk of physical damages by users (i.e. hurts or improper use) (1.2.4.2), and risk of substitution by other data glove with better functionalities (1.2.4.3). Obsolescence of the glasses (1.2.5) arises when the market shows other glasses with better characteristics (like less weight and higher resolution) (1.2.5.1), when users cause physical damages (1.2.5.2) or/and when the glasses become useless due to the introduction of new technological monitor which allow to watch them without glasses (1.2.5.3). (ii) Risk Estimation In order to estimate the risks identified through RBS, we have applied the Probability–Impact grid for the evaluation of the probability and impact of each risk. Even in this case, the probability was evaluated by the observation of potential users interacting with the system (from March 2010 to December 2011), whereas the impact has been evaluated on the base of each problem on the functioning of the whole system and on the effect on consumers’ perception. In particular, after observing the frequency of each problem, we manually caused them to observe the consumers’ reaction (we considered consumers’ exit, complaint, or no reaction) and to evaluate the impact. Thus, for each possible problem we observed consumers’ reaction and evaluated the impact based on the most frequent reactions. The PI grid allows classifying the risks into different ranges. From this analysis, no risks with high probability and high impact emerge. Despite this result, we have identified other high rated risks (classified as ‘‘high–moderate’’): the obsolescence of the screen (1.2.3) that shows the risk of physical damages caused by the users (1.2.3.2), through the observation, we noticed that consumers tend to touch or hurt the screen frequently with consequences for the block or damage of whole system; and the risk of substitution of the actual screen with other screen more efficiently (1.2.3.1), through the observation we noted that this event occurs with a moderate frequency (or probability), in fact in the last few years we observed that the screen have undergone a pretty fast evolution. The possible evolution of the screen and its substitution might cause a significant change in the use of technology, thus the impact on immersive store has been considered high. Another ‘‘moderate–high’’ risk is related to obsolescence of glasses (1.2.5), and regarding the uselessness caused by the introduction of new technological monitors without glasses (frequency related to the previous considerations). In fact, if there is an introduction of a new advanced monitor that allows using immersive store without glasses. The moderate rated risks (moderate probability and moderate impact, low probability and high impact, and high probability and low impact) include also the risks linked to TAM. In particular, risk of social pressure in not using (1.1.4) has been estimated as ‘‘moderate– moderate’’ risk, in fact if they do not have a good experience with the immersive store they tend to influence behaviour of other users negatively. Other risks related to TAM have been estimated as ‘‘low–high’’ risks and include perceived difficult of use (1.1.1) (that have occurred rarely, but with a high impact), risk of uselessness (1.1.2) (that have occurred rarely with negative consequence for consumers’ intention to use further the system, thus caused a high impact), risk of boring experience (1.1.3) (that has been verified seldom, but with a high

impact), risk of privacy (1.1.5) (that have no occurrences) and risk of security (1.1.6) (that have no occurrences). A further risk that has been estimated as ‘‘low–high’’ risk is the risk of slowness of computer (1.2.1.1) (that have no occurrence, even if an excessive slowness could would produce a negative experience for users). The last moderate rated risk is the risk of substitution of data glove with other input devices (1.2.4.1) (estimated as ‘‘high–low’’ risk). This risk has occurred often and in the immersive store due to the frequent developments of new input devices such as motion capture, even if this substitution has not changed the way of using the technology, thus the impact is low. The low rated risks (moderate probability and low impact, low probability and moderate impact, and low probability and low impact) could be the limited capacity memory of the computer (1.2.1.3), glasses with better characteristics (1.2.5.1), and physical damages to glasses caused by users (1.2.5.2). Limited capacity memory (1.2.1.3) has occurred sometimes (for this reason, probability has been considered moderate), the impact is low, because users had no reactions for this event. Other glasses with better characteristics are already available on the market (1.2.5.1), but this event has low impact on technology obsolescence because users continued to use the technology without complaining. Physical damages to glasses caused by users (1.2.5.2) have occurred few times but they had no consequences for the system (when the glasses have been damaged we provided new glasses to the users). Other possible risks even if with low impact and low frequency are: the lack of new functionalities emerging from software tools (1.2.1.2), the improvements of projector’s resolution (1.2.2.1), the improvements of projector’s brightness (1.2.2.2), the improvements of colour contrast (1.2.2.3), physical damages to data glove caused by users (1.2.4.2) and substitution of actual data glove by other data glove better lightweight (1.2.4.3). All these events have no occurrence except physical damages to data glove caused by users (1.2.4.2) which have occurred very rarely. In fact, when this event has happened, a new data glove has been provided rapidly to users. From this analysis, the possible links (or conditional probabilities of occurrence) between risks do not emerge. For this reason, a new tool of risk estimation that allows identifying conditional probabilities of the events and the links between two or more risks (interdependencies between risks) could provide a novel and useful tool for decision-making process. 4.3. Development of the new interdependencies structure Since each risk could have effects on other risks, a new tool taking into account the conditional probabilities between risks would emerge. To achieve this task, we propose a new graphical tool namely Risks Interdependencies Matrix, which allows analyzing the relationships between risks, by deeply understanding the most influenced risks and the most influencing ones. In particular, this matrix graphically shows the risks with many interdependencies without distinguishing their moderate/high rate, thus these risks necessitate of more attention due to the possible impact on the other ones, enhancing the results emerging from the previous analysis. Table 1 shows the interdependencies emerging between the identified risks, as well as the value of these relationships. Interdependencies have been identified with 1 while occurring, in order to make the impact of single risk clearly emerge. Concerning the out of use risks, the threat of perceived difficult of use (1.1.1) influences the occurrence of risk of boring experience (1.1.3), thus a conditional probability exists between these two risks. In fact, if a user believes that the immersive store is too difficult to use, the emerging experience is not pleasant. Similarly, the risk of

0 1

1

1.2.3.1

0

1.2.2.3

0

1.2.2.2

0

1.2.2.1

0

1.2.1.3

1

1

0

1.2.1.2 1.2.1.1

0

1.1.6

1 3

1

1.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.1.6 1.2.1.1 1.2.1.2 1.2.1.3 1.2.2.1 1.2.2.2 1.2.2.3 1.2.3.1 1.2.3.2 1.2.4.1 1.2.4.2 1.2.4.3 1.2.5.1 1.2.5.2 1.2.5.3 Influenced

1

2

4

1

1

1

1

1

1

1

1

0

1.1.5 1.1.4 1.1.3 1.1.2 1.1.1

Table 1 Risks Interdependencies Matrix for immersive store.

1 1 2

1

1.2.3.2

1

1.2.4.1

0

1.2.4.2

1

1.2.4.3

1

1.2.5.1

0

1.2.5.2

1

1

1 0 2 0 0 0 1 0 2 0 0 0 3 2 1 2 0 1 1 1 17

1.2.5.3

Influencing

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boring experience (1.1.3) has relations with the risk of uselessness (1.1.2) and the risk of social pressure in not using (1.1.4); when consumers have a boring experience in using technology, they tend to share the bad experience with other friends, influencing others subsequent behaviours in (not)usage (1.1.4). As a consequence, the boring experience generates also the risk of uselessness of the technology (1.1.2). Concerning the out of date risks, the risk of slowness computer (1.2.1.1) influences the risk of boring experience (1.1.3), because a slow computer affects the speed of human–computer interactions by making it not pleasant. Similarly, limited capacity memory of computer (1.2.1.3) influences the risk of uselessness (1.1.2) and the risk of slowness computer (1.2.1.1). Since each computer has a limited capacity of memory, the memory capacity reduces the system performances (1.2.1.1). A limited capacity of memory generates also the uselessness of immersive store, because it not allows adding new products to the data repository, thus the usefulness of the technology decreases. The risk of screen substitution (1.2.3.1) is linked to the risk of perceived difficult of use (1.1.1), boring experience (1.1.3), substitution of input devices (1.2.4.1) and uselessness of glasses by introduction of new advanced monitors (1.2.5.3). The introduction of new technological screen could cause the substitution of input devices (1.2.4.1); indeed the new monitor might change the use of new input devices (i.e. motion capture). The introduction of new screens without glasses, would avoid their usage (1.2.5.3). The risk of physical damages caused by users to the screen (1.2.3.2) is related to risk of boring experience (1.1.3) and risk of substitution of screen with other new monitors (1.2.3.1). If physical damages occur to monitors, the immersive store becomes impossible to use, thus, users may have a bad experience with technology (1.1.3). Furthermore, the damage may cause the substitution of screen with new monitors (i.e. touch screen monitors or 3D monitor without glasses) in order to introduce a monitor with less users damages risks. Risk of substitution of data glove by other technologies (i.e. motion capture) (1.2.4.1) is linked to the perceived difficult of use (1.1.1). In fact, the substitution of data glove with other new technologies could cause in users a change in their usage behaviour with consequences for the perceived ease of use of the system. Physical damages caused by users to data glove (1.2.4.2) could be one of the main causes that force to substitution of data glove with other technologies (as motion capture) (1.2.4.1) or the substitution with new data gloves with better functionalities or more resistant (1.2.4.3). Regarding the glasses, the risk of substitution by other glasses with better lightweight and resolution (1.2.5.1) may impact the risk of boring experience (1.1.3). The glasses physical damages caused by users (1.2.5.2) may influence risk of substitution by other glasses with better lightweight and resolution (1.2.5.1). Furthermore, the risk of introduction of new technological monitors without glasses (i.e. 3D monitors without glasses) (1.2.5.3) is related to the risk of perceived difficult of use (1.1.1), because the introduction of a new advanced monitor may cause a change in consumer’s usage behaviour, by arising the perceived difficult of immersive store usage. A further finding is related to the risks that emerge as the most influencing and the ones that emerge as the most influenced. The Risks Interdependencies Relations Matrix shows the risk of substitution of the screen (1.2.3.1) as the most influencing risk (it has a direct impact on other three risks) and risk of boring experience as the most influenced risk (this risks is influenced of other four risks). The matrix represents a further tool for identifying the critical risks that could be not emerge clearly from the RBS and PI grid. Since the high rated risks (one or more) have a greater impact on the obsolescence of the immersive store, the more influencing risks could increase their rate, because their occurrence may generate a series of risky events chain. Indeed, risk classified as more influenced risks, allow retailers to understand which risks could be occurred if various risks occurs.

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5. Discussion Since the innovations in the stores generate important changes, wide costs and high level of uncertainty (Fanelli and Maddalena, 2012), an efficient management risk analysis plays a key role for predicting and reducing the possible risks, by modifying the subsequent firm’s behaviour (McGaughey et al., 1994; Alhawari et al., 2011; Holzmann and Spiegler, 2010). Despite the increasing attention in this direction, it is difficult to predict which technology has the most potential to become part of the new points of sale by improving the traditional retail process. In fact, the current studies on the introduction of advanced technologies mainly focus on the risk of acceptance by consumers (Kowatsch and Maass, 2010; Pantano and Servidio, 2012), thus the present study aims at proposing a new framework that focuses on the technology risk management for well performing the technology-based innovation management for retailing. After explaining this framework and embedding it in a existing promising technology, we propose a method for better mapping the risks encountering in innovation systems and resulting in technological change for the traditional points of sale. According to the literature (Solomon et al., 2000; Hillson, 2002; Feldman and Sandborn, 2007), we identified the physical, technical and market risks of internal technological components that might affect the obsolescence of the whole technology. To achieve this task, we used the Risk Breakdown Structure (Hillson, 2002) and the Probability–Impact grid (Ward, 1999). Since these tools do not take into account the interdependencies between risks that would be able to increase the negative effect of each risk, we introduce a further analysis focusing on the risks interdependencies that shows to what extent some risks with low impact in the probability–impact grid acquire more importance, owing to the great influence on other risks. From this analysis, the risk of screen substitution (1.2.3.1), previously evaluated as a moderate–high risk, emerge as one of the most important due to the impact on others. In fact, the risk of perceived difficult of use system (1.1.1), of data glove substitution with other technologies (1.2.4.1), and of introduction of new technologies that do not require glasses with polarized lens (1.2.5.2) are directly affected by this one. Hence, this component of the immersive store would be able to determine the obsolescence of the whole system, and its damages, malfunctioning, improvements have high impact both from a technical point of view and consumers’ usage. Similarly, improvements and investments in this direction will be able to enhance the quality of the whole system and the users’ satisfaction. For this reason, the matrix of interdependencies emerges as a new useful tool for providing further critical information able to reduce the uncertainty concerning the introduction of a new technology and supporting retailers in the decision-making process and risk management, by facilitating the evaluation of the actual influence of any kind of risk on the others. In fact, the interdependencies matrix is able to provide a ‘‘qualitative’’ measure of the risk impact by considering the ‘‘width’’ of the influence of one risk on the others. Furthermore, our findings show the different importance of each risk, owing to the different role of each risk on the obsolescence of the innovation at the point of sale. Hence, the different risks have a different impact on the total obsolescence of the whole system, by acting at a different level. In fact, some risks have a stronger impact on the system, because their occurrence may cause the malfunctioning of the entire system or the occurrence of other risks (as emerging from the matrix of interdependencies). Since the introduced innovations have a different importance according to the elicited changes (Marquis, 1969; Damanpour and

Wischnevsky, 2006; Sen and Ghandforoush, 2011), each innovation can be considered ‘‘incremental’’ or ‘‘radical/disruptive’’. Similarly, also risks can be defined as ‘‘radical’’ or ‘‘incremental’’ according to the different effects on the obsolescence risk of the innovative technology to be introduced. While the technical improvements of the systems can regard small enhancements in the features, we might face an ‘‘incremental’’ risk for the system obsolescence. If a strong improvement in the system functionalities is implemented (for example a deep change in payment methods), we encounter a ‘‘radical’’ (or disruptive) risk for system obsolescence. Hence, the high rated risks in the PI matrix and the ones with the strongest impact on the others can be considered as radical risks, due to the weight of their effect on both the system functioning and the acceptance by consumers. As the consequence, decision makers would take more into account the radical ones while evaluating the obsolescence risk for the adoption choice of a new technology to be introduced in the point of sale. If they are combined with the information emerging from the analysis of Technology Life Cycle, retailers achieve more information for reducing uncertainty and better evaluating the investment on this technology. In our case study, the screen is the core element of the immersive environment, thus its malfunctioning, damage or improvements have impact both for consumers’ experience and other components. As a consequence, its effect can be disruptive from the obsolescence risk analysis, by prompting retailers to focus especially on this element for reducing the global obsolescence risk emerging from the adoption of immersive technologies in the points of sale. Furthermore, our findings show how the radical innovations such as the immersive stores may encounter both incremental and radical risks able to affect the whole technology in different ways. In particular, each technology-based innovation to be introduced in the point of sale would encounter incremental and radical risks which are not mutually exclusive (rather, usually it could have both risk’s types) that should be exploited in with different modalities; particular attention have to be paid to the radical risks and interdependencies (conditional probabilities) which may generate them.

6. Future works Owing to the great number of risks involved in the introduction of innovations at the points of sale with emphasis on the critical role of obsolescence risk, tools for the efficient management are necessary for reducing the uncertainty. In this work, we propose a further tool for supporting managers, by considering the impact of each risk on the others. This tool supports the evaluation of the real impact of a risk: the interdependencies risks relationship matrix. In particular, we tested the tool on a new technology (the immersive store) not introduced yet in the stores, thus still in the early phase of the life cycle. Furthermore, from our analysis the different weight of each risk on the total obsolescence risk of the whole system emerges. In fact, some risks are more critical due to both frequency of their occurrence and the impact on the system and impact on other single risks. For this reason, we introduced the concept of radical and incremental risk, for better distinguish the value of these ones and suggest retailers (or decision-makers) to focus their effort on the radical/disruptive ones. Although important issues emerged from our work, there are some limitations which should be taken into account. The first concerns the total amount of the investigated risk. In fact, the risk analysis does not focus on all the possible risks for immersive store, by emphasizing only the most important ones. For instance, there are more risks related to the malfunctioning or damage of smaller components such as the cables for electricity or batteries concerned that are not included in this work. Another limit

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