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«Abstract We conducted a qualitative study of Nokia to understand its rapid downfall over the 2005–2010 period from its position as a world-dominant ...»

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Izard, 2009). A second reason that we might expect fear to be central to innovation is that other past-oriented negative emotions that people may feel during the innovation process—such as shame or envy—can turn into fear. When people experience such a negative past-focused emotion and then imagine a similar future situation, they may fear the same negative outcome occurring again (cf.

Dane and George, 2014). For example, one can feel shame after having given a bad presentation and therefore fear that the next presentation will also fail.

Because fear is related to the perception of threat, the literature on threat rigidity could inform us. The original threat-rigidity theory hypothesized some mechanisms that cause organizations to respond in a rigid way to an external threat (Staw, Sandelands, and Dutton, 1981), including increased emotional arousal, which narrows top managers’ thinking, constrains communication, and makes middle managers more dependent on them. The hypothesized mechanisms of the threat-rigidity theory remain largely speculative, however, and have not been empirically verified. The original theory assumes that the whole organization would perceive the threat in a similar way and develop homogeneous cognitive-emotional reactions that cause collective rigidity. Empirical tests have measured only the presence of the threat perceptions with proxies such as return on assets (Greve, 2011), bargaining cycles (Griffin, Tesluk, and Jacobs, 1995), changes in the studied firms’ funding sources (D’Aunno and Sutton, 1992), and changes in the performance of an acquired unit (Shimizu, 2007) rather than various groups’ actual perceptions of the threat and resulting behavior inside the organization. This is problematic because some case studies have suggested that various units in the same organization could differ in their perceptions of whether a new technology constitutes a threat or an opportunity— or whether it is simply irrelevant (Tripsas and Gavetti, 2000; Gilbert, 2005).

Gilbert (2005) showed that different threat perceptions influenced how independent units performed their tasks. These differing perceptions could also influence the emotions people feel in the different units and how these units consequently interact with one another in the organizational innovation process.

Such differences in threat perceptions among organizational groups might arise due to the influence of their varied positions in the organizational structure. If groups specialize in different tasks and focus on different matters, they probably perceive things differently and regard some matters as more important than others. Differing emotions between groups may thus arise (cf. Lazarus, 1991) as a result of the structural distribution of attention (Ocasio, 1997).

Because of the social psychology of organizational structures, people may also perceive threats to their status and power within the focal structure, which 6 Administrative Science Quarterly XX (2015) can trigger strong emotions. A key characteristic of the organizational hierarchy is that it confers unequal formal status on various organization members through titles, roles, and responsibilities (e.g., Simon, 1947; Magee and Galinsky, 2008). Such formal status interacts with informal status to determine individuals’ power— the extent of their control over resources that other members value (Pfeffer, 1981). Many organization members thus value status and compete with one another to reach or maintain high organizational status (Rosenbaum, 1979; Magee and Galinsky, 2008) and may feel strong emotions if they perceive related threats (cf. Lazarus, 1991). Less is known, however, about how such potential emotions related to seeking status influence their behaviors during the innovation process.

People’s status also influences the emotions they experience (Ellsworth and Scherer, 2003: 580). In particular, low-status people may have a hard-wired, evolutionary-based fear of high-status people because, in the past, individuals with higher status would have controlled resources critical for survival (KishGephart et al., 2009). Status differences can also determine the sequence of emotions felt among individuals (cf. Hareli and Rafaeli, 2008). For example, status can turn anger into fear: if an employee expresses anger toward a superior, he or she is likely to respond in kind, which causes the employee to feel fear (cf. van Kleef, De Dreu, and Manstead, 2004). Status differences could thus cause people to differ in how they construe a threat and to experience different emotions, while people of similar status are more likely to perceive a threat in a similar way and to experience similar emotions.

Likewise, though the structural distribution of attention likely generates between-group differences in emotions, it could foster within-group similarities. Members of the same group likely attend to similar things and perceive that those things have similar implications for them. Group members may thus experience emotions similar to their peers consistently over time regarding issues that they see as key to the group’s welfare (Elfenbein, 2014; Menges and Kilduff, 2015). For example, if a group is exposed to fast time pacing in product development, members may develop a shared fear of missing deadlines (cf. Brown and Eisenhardt, 1997). Thus, just as group members may share similar mental models (Mathieu et al., 2000; Healey, Vuori, and Hodgkinson, 2015), they may also share similar emotions (see Mackie, Devos, and Smith, 2000; Huy, 2011; Elfenbein, 2014; Menges and Kilduff, 2015).

Shared emotions would cause many individuals to experience similar action tendencies (e.g., Elfenbein, 2014; Menges and Kilduff, 2015) and might make them behave in similar ways as a result of socialization processes (cf. Smith, Seger, and Mackie, 2007; Huy, 2011). But little empirical research has examined whether and how macro-level organizational structures and other factors generate similar or different emotions within and between key organizational groups and how such shared emotions influence the organizational innovation process and its outcome.

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(Gartner, 2009). Nokia had bountiful economic and intellectual resources, had dominated the industry for years with a stream of innovative models, and was frequently put forward as a world-class exemplar of strategic agility (Doz and Kosonen, 2008; Steinbock, 2010). But by the fall of 2010, despite significant efforts, Nokia had failed to introduce a smartphone to match the iPhone, creating the perception that it was losing the battle against Apple. Nokia replaced its chief executive officer (CEO), abandoned software development, and became a mere hardware provider. It surrendered its position as the leading smartphone provider and ultimately exited the mobile phone business.

Context The mobile phone industry has grown rapidly during the past 20 years, and its trajectory has been divided into several distinct technological phases. By 2005, the industry was moving toward third-generation (3G) radio technologies that enabled faster Internet connections. In 2007, Apple introduced the iPhone, which lacked 3G support but still promised mobile Internet, in the eyes of the mass market, with its large touch screen and advanced user interface enabled by iOS, an exclusive operating system (OS) based on the OS Apple had been developing for years for its computers. This move was a major discontinuity for the whole industry: for the first time, differentiation lay in software rather than radio technology—see table 1 for key market events and changes in market shares. This shift presented a growth opportunity to companies such as Apple and Google, which were already strong in software development; other traditional players had to scramble to develop software capabilities that were radically new to them. Samsung, seemingly the only traditional player to prosper after the software revolution, chose to adopt Google’s Linux-based Android OS (an open-source software). But Nokia, hoping to ensure sustainable profit, decided to continue improving its proprietary OS.

Top managers’ belief that Nokia should remain autonomous in software terms was supported by its past achievements (Cord, 2014). Even its early phone models had software-enabled functions such as phone books and text messaging. By 2005, Nokia’s primary smartphone OS was Symbian, which both internal and external developers generally described as difficult to work with. Additional complexity arose from Nokia’s simultaneous development of Internet services to be integrated with Symbian. Nokia had also been exploring the use of Linux in mobile devices. The result, an OS named MeeGo, was intended to replace Symbian after several product generations. Nokia’s top managers thus believed it had all the elements in place to develop good software and remain the leading smartphone provider. But consumers’ responses to Nokia’s phones became increasingly negative between 2008 and 2010 because the improvements in product quality seemed modest compared with challengers’ offerings.

Data Collection Our primary data came from private interviews conducted between November 2012 and February 2014. We carried out a total of 76 interviews with Nokia’s top managers (TMs), middle managers (MMs), engineers, and external experts, as detailed in table 2. Consistent with Huy (2001, 2011), we defined MMs as 8 Administrative Science Quarterly XX (2015) Table 1. Market Shares and Key Events in the Mobile Phone Sector (including Smartphones), 2007–2010*

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* Data are presented as ‘‘all mobile phones (only smartphones)’’; ‘‘x%’’ means that the market share was too small to be reported (i.e., included in the category ‘‘others’’). Sources: http://www.gartner.com/newsroom/id/612207;

Chaplinsky and Marston (2011); http://www.gartner.com/newsroom/id/1543014; http://www.gartner.com/ newsroom/id/910112; http://www.quirksmode.org/blog/archives/2011/02/smartphone_sale.html.

those who were two levels below the CEO and one level above engineers and front-line workers. The interviews were carried out in three rounds, and we analyzed the data between each round to inform subsequent interviews.

Several key informants who were closely involved with the mobile-phone business, including the CEO, vice presidents, R&D managers, and strategy directors, were interviewed multiple times.1 We purposefully sampled the Nokia had two CEOs during the period of our study. When we speak of ‘‘the CEO,’’ we refer to the second one, whose tenure as CEO lasted from 2006 to 2010. He worked in several executive positions at Nokia before becoming CEO. When we speak of ‘‘the chairman,’’ we refer to the first CEO, whose tenure as CEO lasted from 1992 to 2006. He was chairman of the board from 1999 to

2012. While the chairman was less active in daily management from 2006 onward, we bring up his role because (1) our informants highlighted how his behaviors had shaped Nokia’s culture, (2) he still exerted influence as chairman until the end of our study period, and (3) the dynamics that we describe in the findings had already started during his tenure as CEO.

Vuori and Huy 9 Table 2. Informants and Interviews

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* ‘‘–’’ means that we did not show the model to the individual due to timing (early stage of the study) or schedule (short interview).

À This EVP emailed us his/her thoughts multiple times and confirmed that top managers lacked understanding of technological capability, but his/her answers are not included in the count of interviews.

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example, top managers told us they pressured middle managers for faster performance, and middle managers also told us how they felt pressured by top managers. Second, we relied on informants who were particularly knowledgeable about the relevant events and for whom the events were personally important, thus improving memory accuracy. Third, we used specific interview techniques such as precise ‘‘courtroom’’ questioning and event tracking, which provide accurate and convergent information among informants (Eisenhardt, 1989). We asked individuals to describe concrete examples and events, obliging them to rely on their episodic memory, which provides more comprehensive accounts (Tulving, 2002) and increases recall accuracy (cf. Fisher, Geiselman, and Amador, 1989; Miller, Cardinal, and Glick, 1997; Fisher, Ross, and Cahill, 2010). Episodic memories, which may rely on a separate memory system, complement more-generic recollections of mental states because they contain specific details of not only what was happening or felt but also exactly when and where (Tulving, 2002). In this way our data were triangulated, making them more trustworthy. For example, it is more comprehensive when a person remembers a specific instance of when and where he or she felt fear, rather than simply recalling having felt generally afraid in the past. Episodic memories may also be more reliable than retrospective survey measures because they do not oblige informants to select a discrete category to characterize their memory. We also asked informants to describe factual manifestations of mental states, which are more robust to retrospective biases than memories of mental states (e.g., Miller, Cardinal, and Glick, 1997). Fourth, we validated subjective accounts with factual and prospective sources when possible; we reviewed nearly 1,000 articles and books about Nokia, and several informants also shared confidential internal reports, presentation slides, e-mails, and notes that corroborated our findings.

Data Analysis We used the method described by Gioia, Corley, and Hamilton (2013) to guide our data analysis. Novel understanding can often be gained by carefully investigating how various participants of an organizational process experience events, and the authors suggested some practices that bring ‘‘qualitative rigor.’’ As is typical of inductive research, our analytical process was iterative and overlapped with data collection, but several phases can be recognized. During these phases, we developed increasingly refined inferences of novel theoretical mechanisms from our data.

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