A Relational Account of the Causes of Spatial Firm Mobility

It is accepted in the literature that exchanges within networks have an ongoing social structure that both enables and constrains the behavior of its members (Pfeffer and Nowak 1976; Uzzi 1996). However, most research in inter-organizational settings has focused on the enabling effects of networks and network structures only, even though some noteworthy exceptions exist (e.g. Romo and Schwartz 1995; Singh and Mitchell 1996). A possible constraining effect of network participation is spatial lock-in, also known as spatial inertia, of a firm. Following Resource Dependence Theory (Pfeffer and Salancik 1978), it can be argued that a firm that makes extensive use of knowledge resources possessed or controlled by external actors for its innovative processes can become dependent on these actors. By themselves, the relationships in which these dependencies exist are non-spatial. However, since geographical proximity is assumed to facilitate the successful exchange of (especially tacit) knowledge through inter-organizational relationships (IORs) (Bretschger 1999), dependency on other firms located in the same region can also lead to dependency on a certain geographical location, and thus to spatial lock-in (Stam 2003). The IORs that are enabling for the firm in terms of its innovative processes act, at the same time, as constraining factors for the spatial behavior of the firm. Similar reasonings can be found in the literature on Territorial Innovation Models (Moulaert and Sekia 2003), which indicates that economic embeddedness in a region can be beneficial for the performance of firms. However, this embeddedness can also lead to dependence on localized inputs and production factors. Due to these dependencies, a firm can become very unlikely to relocate, even if doing so is beneficial from a cost perspective. As Romo and Schwartz state: “Firms are usually too dependent on the material, political and social resources available in the local production culture to risk departure, even when production costs might be substantially reduced (Romo and Schwartz 1995:874).†There currently is, however, only weak empirical evidence for the proposed relationship between the level of (local) embeddedness and a firm’s propensity to relocate. Moreover, several authors even propose that geographical distance in IORs is becoming irrelevant since it effects can be replicated by ICT (Morgan 2004), or high levels of organizational or technological proximity (Kirat and Lung 1999). If this is indeed the case, then participation in localized innovative IORs will have no effect on the spatial behavior of firms, since a firm can operate exactly the same on a different geographical location. The main goal of this research is to provide empirical insights into the effects of a firm’s level of participation in innovative (localized) inter-organizational relationships (IORs) on its propensity to relocate. Based on the above, the following research question has been formulated is “To what extent is the level of embeddedness of a firm in (localized) innovative inter-organizational relationships of influence on its propensity to relocate?†Answering this research question adds to the insights about the constraining effects of networks by focusing on the spatially constraining effect of inter-organizational relationships. This research question will be answered based on a data from a survey among Dutch automation service firms in 2006. In line with earlier research (c.f. Van Dijk and Pellenbarg 2000; Brouwer et al. 2004) an ordinal logit model will be used to relate the relocation propensity of a firm to that firm’s participation in localized innovative IORs, the strength of these IORs, and the level of geographical, organizational and technological proximity. It also provides insight into the question whether or not high levels of technological and organizational proximity can negate the need for geographical proximity in inter-organizational collaboration (Boschma 2005). References: Boschma, R. A. (2005). "Proximity and innovation: A critical assessment." Regional Studies 39(1): 61-74 Bretschger, L. (1999). "Knowledge diffusion and the development of regions." Annals of Regional Science 33(3): 251-268 Brouwer, A. E., I. Mariotti and J. N. van Ommeren (2004). "The firm relocation decision: An empirical investigation." Annals of Regional Science 38(2): 335-347 Van Dijk, J. and P. H. Pellenbarg (2000). "Firm relocation decisions in The Netherlands: An ordered logit approach." Papers in Regional Science 79(1): 191-219 Kirat, T. and Y. Lung (1999). "Innovation and proximity - Territories as loci of collective learning processes." European Urban and Regional Studies 6(1): 27-38 Morgan, K. (2004). "The exaggerated death of geography: Learning, proximity and territorial innovation systems." Journal of Economic Geography 89(1): 3-21 Moulaert, F. and F. Sekia (2003). "Territorial innovation models: A critical review." Regional Studies 37(3): 289-302 Pfeffer, J. and P. Nowak (1976). "Joint-ventures and interorganizational interdependence." Administrative Science Quarterly 21(3): 398-418 Pfeffer, J. and G. R. Salancik (1978). The external control of organizations: A resource dependency perspective. New York, Harper and Row Romo, F. P. and M. Schwartz (1995). "The structural embeddedness of business decisions: The migration of manufacturing plants in New York state, 1960 to 1985." American Sociological Review 60(1): 874-907 Singh, K. and W. Mitchell (1996). "Precarious collaboration: Business survival after partners shut down or form new partnerships." Strategic management journal 17(2): 99-116 Stam, F. C. (2003). Why butterflies don't leave: Locational evolution of evolving enterprises. Utrecht, Utrecht University Uzzi, B. (1996). "The sources and consequences of embeddedness for the economic performance of organizations: The network effect." American Sociological Review 61(4): 674-698

Would You Trust a (Faulty) Robot? : Effects of Error, Task Type and Personality on Human-Robot Cooperation and Trust

How do mistakes made by a robot affect its trustworthiness and acceptance in human-robot collaboration? We investigate how the perception of erroneous robot behavior may influence human interaction choices and the willingness to cooperate with the robot by following a number of its unusual requests. For this purpose, we conducted an experiment in which participants interacted with a home companion robot in one of two experimental conditions: (1) the correct mode or (2) the faulty mode. Our findings reveal that, while significantly affecting subjective perceptions of the robot and assessments of its reliability and trustworthiness, the robot's performance does not seem to substantially influence participants' decisions to (not) comply with its requests. However, our results further suggest that the nature of the task requested by the robot, e.g. whether its effects are revocable as opposed to irrevocable, has a signicant im- pact on participants' willingness to follow its instructions

“Do you still trust me?” Effects of Personality on Changes in Trust during an Experimental Task with a Human or Robot Partner

In the current study, we investigated the effects of dispositional variables on self-reported trust and suspicion perceptions of one’s partner in a maze-running task. Dispositional variables affect the extent to which people perceive and encode information in their environment. Prior research has shown that dispositional variables interact with situational variables in expressing behaviors. In order to test the effects of three dispositional variables (i.e., dispositional trust, dispositional distrust, and dispositional suspicion) on self-reported trust and suspicion perceptions towards a partner (a human or a Nao robot), we ran two discontinuous growth models. Overall, we found that participants’ trust towards their partner decreased when the partner engaged in untrustworthy behaviors as expected. In addition, changes in trust perceptions towards the partner were predicted by participants’ level of dispositional trust. These results have implications for studying the effects of dispositional variables on context-dependent trust perceptions within the trust process

Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnaires

A significant challenge to measuring human-automation trust is the amount of construct proliferation, models, and questionnaires with highly variable validation. However, all agree that trust is a crucial element of technological acceptance, continued usage, fluency, and teamwork. Herein, we synthesize a consensus model for trust in human-automation interaction by performing a meta-analysis of validated and reliable trust survey instruments. To accomplish this objective, this work identifies the most frequently cited and best-validated human-automation and human-robot trust questionnaires, as well as the most well-established factors, which form the dimensions and antecedents of such trust. To reduce both confusion and construct proliferation, we provide a detailed mapping of terminology between questionnaires. Furthermore, we perform a meta-analysis of the regression models that emerged from those experiments which used multi-factorial survey instruments. Based on this meta-analysis, we demonstrate a convergent experimentally validated model of human-automation trust. This convergent model establishes an integrated framework for future research. It identifies the current boundaries of trust measurement and where further investigation is necessary. We close by discussing choosing and designing an appropriate trust survey instrument. By comparing, mapping, and analyzing well-constructed trust survey instruments, a consensus structure of trust in human-automation interaction is identified. Doing so discloses a more complete basis for measuring trust emerges that is widely applicable. It integrates the academic idea of trust with the colloquial, common-sense one. Given the increasingly recognized importance of trust, especially in human-automation interaction, this work leaves us better positioned to understand and measure it.Comment: 44 pages, 6 figures. Submitted, in part, to ACM Transactions on Human-Robot Interaction (THRI

A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids

acceptedVersio

A proposed psychological model of driving automation

This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation