1 Introduction

Automated-guided vehicles (AGVs) represent a popular and emerging technology for increasing the degree of automation in intralogistics [1]. According to the VDI 2510 standard, AGVs are automatically controlled vehicles predominantly used for the in-house transportation of materials [2]. Current research on AGVs is mainlyfocused on solving technical and functional challenges such as path planning, sensor technology, or control systems [3-5]. In practice, however, companies also face organizational challenges when introducing AGVs, such as a lack of acceptance among the workforce, e.g., when employees fear that their working routines will be drastically changed and their jobs rationalized [6]. These issues have received little attention in the literature on AGVs so far, although unions already considered these vehicles as a major threat to jobs when they were first developed in the 1950s, leading to acts of sabotage [7]. The lack of contemporary research on these issues is both surprising and crucial, as technology acceptance and human-technology trust have been identified as key success factors for the introduction of similar technologies such as (collaborative) robots or artificial intelligence (AI) applications [8-10].

The introduction of such advanced technologies into an existing production environment regularly constitutes a remarkable organizational change process, whose outcomes often deviate from ex-ante plans [11]. While high-level managers view technology-induced change as beneficial and even necessary for the continuation of the business and the improvement of the market situation in the face of external challenges, employees affected by these changes and even project leaders as so-called change recipients are primarily concerned about the practical changes to their well-established and valued daily routines and about the associated uncertainty and cognitive load [12]. Given these often-underestimated differences in individual perspectives on change, technology adoption should be seen as part of more general organizational change processes and be accompanied by systematic change management (CM) initiatives to foster change readiness ex-ante. However, the scientific literature has neither compared and analyzed different attitudes towards AGVs, nor applied or discussed CM approaches in the specific context of AGV implementation processes. Consequently, it remains unclear what factors drive the success of an AGV implementation in practice, and what measures can be taken to promote employee acceptance and support a sustainably successful change process. In this article, we attempt to address this research gap by conducting a quantitative survey of production and logistics workers, project leaders, and managers (n = 89) in ten German companies that have already introduced AGVs in their production. To the best of our knowledge, this is the first study to investigate and compare the viewpoints of both operational and managerial staff on the real-life introduction of AGVs and to analyze the impact of managers’ perspective taking accuracy, i.e., their ability to accurately anticipate what their employees consider relevant to an AGV introduction process. In the following, Chapter 2 presents the necessary theoretical background on AGVs, on issues of employee acceptance, and on organizational change and change management. Each subsection identifies the relevant research gaps and presents the corresponding research questions, which we then address empirically. Chapter 3 explains the method and materials for the empirical study. The results derived from the empirical work are presented and discussed in Chapter 4. Chapter 5 describes the methodological limitations of the study, before Chapter 6 presents the conclusions of our research.

2 Theoretical background

2.1 Automated-guided vehicles (AGVs)

The ISO 8373:2021 standard describes an AGV as a “mobile platform following a predetermined path” [13], whereas mobile platforms are “examples of [the] mechanical structure of robots” [13]. Despite being considered as service robots in several academic publications [e.g. 14, 15], strictly speaking, AGVs themselves are robotic devices instead of robots, as they are unable to navigate on their own, but instead, follow “predetermined paths indicated by markers or external guidance commands” [16]. In this sense, AGVs could also be distinguished from mobile robots, which by definition are capable of moving under their own control [13]. Other sources emphasize that AGVs are a subcategory of (autonomous) mobile robots (AMR), which are mostly used in industrial contexts and especially in intralogistics for indoor transport [17, 18]. Terms such as self-guided vehicle (SGV), laser-guided vehicle (LGV), or guided carts have a very similar meaning to the term AGV [17, 19]. Due to the imprecise definitions, the terms are often used interchangeably in theory and practice [3, 5, 17].

For locomotion and navigation, it requires the equipment with data transmission and positioning features and the integration with a central guidance control system to implement an automated-guided vehicle system (AGVS) [2]. Meanwhile, it is more common in practice to speak of an AGV implementation, which tacitly assumes that the central components are already present or will be installed in addition. Unlike other mobile floor-supported systems such as remote-controlled manipulator vehicles or conventional forklift trucks, AGVs are not controlled by a human operator [2].

After their development in the 1950s and a first wave of euphoria about the use of AGVs, practitioners’ interest waned significantly until a second wave started in the early 2000s, driven by both technological advances and external circumstances [6, 20]. On the one hand, the implementation of AGVs became cheaper and more efficient due to WIFI-based data transmission to the corresponding guidance control system, higher vehicle speeds, and improved navigation and control procedures through new sensor technology and personal computers [20]. On the other hand, the increasing demand for individualization and customer orientation emphasized the need to implement flexibly adaptable AGVs instead of more static conveyor belts from a management perspective [21, 22]. The operational challenges posed by the introduction of new product variants or fluctuating production utilization can be met relatively easy by adapting the AGVs, clearly demonstrating their relevance for customized mass production [20]. Accordingly, the use of AGVs has been identified as the second most mentioned trend in intralogistics just behind self-organized conveyor systems and 43% of the domain experts considered AGVs as a trend worth investing in [23].

However, although the use of AGVs promises significant reductions in operating costs compared to conventional operation with forklift trucks and manpower, it also requires a much higher investment [24]. According to [20], the average investment for an AGVS, consisting of the vehicles and the central control and guidance units, is around €650,000. Furthermore, experience with similar technologies shows that high potentials do not necessarily lead to high market penetration due to unsuccessful introduction processes [25]. It is therefore important for company managers to know in advance exactly what benefits they want to achieve with the implementation of AGVs and which factors will promote the achievement of these goals, i.e., which factors will make the introduction process a success story. However, since the introduction of AGVs is usually not a recurring operation, particularly within small and medium-sized enterprises (SME), top-level managers and project leaders usually lack previous experience with AGV implementation. Furthermore, only a few studies [e.g. 26–28] provide practical suggestions for companies on what to consider during the introduction process. Consequently, the lack of a comprehensive framework can easily lead to divergent individual understandings of the most influential factors that need to be considered. Such discrepancies make it difficult to define, communicate, and execute a clear and unambiguous technology implementation strategy. Therefore, at the managerial level, we aim at answering the following research question (RQ):

  • RQ1: What are the objectives that top-level managers and project leaders are trying to achieve with the implementation of AGVs, and what do they consider to be the success factors for achieving these objectives when implementing AGVs?

2.2 Employee acceptance

In related research, it is well known that a lack of acceptance can lead to a silent refusal to use a technology at the attitudinal level, but also to active resistance at the behavioral level [11, 29]. The latter is reflected in the manipulation of technical devices to reduce the efficiency of the implemented technical systems or in the deliberate damage of AGVs, e.g., by bumping into an AGV with the forklift truck [20]. Such behavior has been frequently observed and documented with regard to various types of interactive robots or autonomous vehicles [30-32] and is in the tradition of the Luddites’ fight against the technologization of the workplace [33]. In the case of AGVs, it is comparatively easy to disrupt the proper operation of these vehicles by standing in their way or blocking their usual paths, as workers usually have to share routes with the AGVs [20]. Although these examples clearly represent unintended interactions, they also point to the consideration of AGVs as an interactive technology. Therefore, although AGVs are often associated with automation technology, they can also be considered from a human-machine interaction (HMI) perspective. HMIs are usually divided into several subcategories, namely, coexistence, cooperation, and collaboration, depending on the characteristics of the interaction [34, 35]. Contrary to visions of deserted factories, AGVs usually coexist and operate in the same workspace as workers without any physical separation [36]. When workers take goods from the AGV, the criteria for cooperation, which is an even closer form of interaction, are also met. Consequently, although the AGVs’ interaction interfaces in regular working mode might be less complex compared to human-robot or human-AI interfaces, considering AGVs from an HMI perspective underlines the relevance of acceptance issues to prevent operational staff from disturbing the proper and intended operation of AGVs. Accordingly, 105 domain experts rated user acceptance as the fourth most important success factor for digitization projects in intralogistics with 60% of the surveyed experts considering user acceptance as “relevant” or “very relevant” [23]. They only considered communication between team members (68.2%), data security (64.2%), and training of the IT workforce and employees (61.1%) as slightly more critical for success. Furthermore, acceptance issues come to the fore in the sense that the introduction of AGVs changes the role of the workers and implies manifold changes in organizational structures, command hierarchies, and production processes [7]. However, although acceptance issues and the organizational challenges posed by the implementation of AGVs have been raised for many years, only a few scholars have taken up this subject and added further elaboration. While many publications on AGVs mention acceptance as a relevant issue [3, 7, 23, 28, 37, 38], human factors have rarely been systematically and holistically explored and considered in the literature on AGVs. In the context of comparable technology introduction, technology acceptance models (TAM) [39] are frequently used as frameworks to address these issues. More modern and advanced versions of these models (such as UTAUT [40] or TAM3 [41]) include considerably more items and thus demonstrate the multitude of facets and prerequisites of acceptance. This is particularly true for TAMs and frameworks on success factors dedicated to interactive technologies such as collaborative robots [8, 42]. However, while these models are based on assumed cause-and-effect chains and on measured correlations between different constructs, it remains unclear, which aspects are most important for achieving a sufficient level of worker acceptance and should therefore be explicitly addressed by managers. Furthermore, these models fail to sufficiently integrate relevant related literature on technology-induced organizational change in a broader sense [11]. In addition, although acceptance is often mentioned as a critical factor, we are not aware of any systematic empirical data reaching beyond qualitative single-case studies that shed light on workers’ perceptions of how well acceptance factors have been considered in concrete AGV implementation processes. Such an analysis promises to identify important antecedents of acceptance and highlight shortcomings in the execution of practical projects to implement AGVs. Hence, in this piece of research, we seek to answer the following RQs by surveying operational staff and analyzing their perspective on AGV introduction processes:

  • RQ2a: What do operational staff (production and logistics workers) consider as relevant factors for acceptance of AGV?

  • RQ2b: How do operational staff (production and logistics workers) rate the degree of fulfillment of acceptance factors in AGV implementation projects in their company?

Additionally, previous research has highlighted that employees sometimes fear losing their job as a result of emerging technologies in intralogistics [1, 20]. Such fears are known to significantly hinder technology acceptance, whether rational or not [42-45]. Unlike related technologies, AGVs affect both logistics and manufacturing workers. However, these two groups of workers relate to the technology differently, in the sense that production workers interact with AGVs, whereas logistics workers do not only interact with them, but could potentially also be completely replaced by them in the future. As a result, forklift operators in particular view AGV often as “job killers” [20]. Analogous to the introduction of collaborative robots, which can operate either in a collaborative or in a stand-alone mode without human involvement, AGVs represent either collaborators or competitors for human labor [46]. Based on this argument, we analyze the prevalence and the influence of the perceived threat of job loss on workers’ satisfaction with the implementation of AGVs, distinguishing between logistics and manufacturing workers’ attitudes:

  • RQ2c: How does the perceived threat of job loss affect production and logistics workers’ satisfaction with AGV implementation projects?

2.3 Organizational change management and perspective taking

Change management (CM) is a common term for interventions to identify and implement the best way to achieve a desired state of an organization based on its current starting point [47]. In this sense, CM is explicitly not about identifying reasonable company goals, but about defining and designing the way to achieve the goals set. This also implies that CM is “primarily directed inward, that is, toward the members of the organization or company undergoing change” [47]. The need for CM has been emphasized in recent years due to the ever-changing external circumstances and consumer demands, especially as the pace of change has increased, inter alia driven by technological advancements [48]. Strikingly, this has even led [49] to classify “the leadership of organisational change” as the most relevant task for management nowadays. However, despite its increasing and widely acknowledged relevance, CM approaches suffer from a weak empirical evidence base resulting in partly contradictory guidelines and conflicting approaches that hinder the positive impact on change processes [48]. For example, one of the most influential and fundamental CM approaches is the “change at three phases” model. According to this model, the first step is to create motivation and readiness for change in an unfreezing phase, then to actually implement the changes by developing new patterns of action, and finally to freeze the changed situation by integrating the changed patterns into daily routines [47, 50]. However, it seems that the model has somehow developed a life of its own over the years, as its reception today differs greatly from the original concepts and has contributed to promoting an oversimplified understanding of the underlying ideas [51].

Technology-driven organizational change processes take place in a specific organizational context and involve different persons. Literature on organizational change commonly refers to four different themes which categorize every change process [52] and which inspire the model on the readiness of organizational change (MROC) [53, 54]. In this model, the content describes the characteristics of the intended change, the context describes the environment in which the change takes place, and the process encompasses the steps to implement the change. In contrast to the initial segregation [52], the fourth theme does not focus on criteria for monitoring successful organizational change, but is replaced by individual attributes of the persons affected by the change. According to MROC, these interrelated factors determine the beliefs and actions of individuals in relation to impending change and thus represent their change readiness [53]. With regard to the focus of this article, the characteristics of AGVs as the relevant technology describe the content, while the respective companies that want to implement the AGV represent the context. The steps of planning, designing, implementing, and monitoring the new AGVs characterize the relevant process, which is shaped by the decisions and the behavior of respective project leaders and should be accompanied by CM initiatives. The individual personality traits of the affected employees within the companies are another essential building block of the change process but have been less studied, as outlined above.

In this context, and drawing on the philosophy of socio-technical systems, the involvement of operational staff already in the planning processes for organizational change is often discussed as a promoter of acceptance [7]. Workers want to feel informed about upcoming changes and demand a transparent communication about the impact of technology introduction [8]. Apart from these more general requirements, the heterogeneity of workforces and their particular attitudes toward technology make it difficult to predict which management initiatives they expect and value most. In addition, the organizational changes affect operational and management staff in a different manner, resulting in divergent attitudes and concerns with regard to AGV implementation [7]. This is a key challenge for project leaders who should ideally incorporate existing needs and concerns into their management activities. Therefore, they need to be able to take the perspective of their employees and consider their main wishes and fears [55]. Perspective taking has been a popular research topic of social psychologists for decades, who have linked this ability to empathy, prejudice reduction, altruism, and social cooperation, only to name a few aspects [56-59]. Perspective taking can thereby be defined as an “active cognitive process of imagining the world from another’s vantage point or putting oneself in another’s shoes in order to understand their visual viewpoint, thoughts, motivations, intentions, and/or emotions” [60]. Additionally, [61] stress that active perspective taking is an intentional, goal-directed process aimed at understanding another’s mental states and emotions without judging them, which can be driven by either altruistic or egoistic motivations. Furthermore, despite all the good intentions and efforts, the attempt to understand a counterpart is not necessarily crowned with success. Instead, one might fail to properly observe and understand another’s mental states leading to a low perspective taking accuracy and effectiveness [60, 61].

More recently, this issue has been taken up in the management literature, which increasingly recognizes the relevance of perspective taking as a necessary interpersonal skill for managers and leaders in organizations, with “important benefits on a wide range of managerially- and organisationally-relevant domains” [60]. For instance, a recent study in the context of infrastructure megaprojects, which tend to generate negative attitudes among affected local communities, identified perspective taking skills as a prerequisite for a deeper stakeholders engagement and thus project success [62]. In addition, perspective taking has been empirically identified as a critical skill in negotiations, whose positive impact exceeds by far the influence of an empathic stance [63]. Although previous research has provided evidence for the positive impact of perspective taking in the workplace and has highlighted its particular benefits in terms of CM initiatives [61], still, more research is needed on the impact of perspective taking in organizational contexts, especially as the existing evidence originates from laboratory studies with limited generalizability to the real-life processes in organizations [60]. The lack of contemporary research is even more significant when it comes to the relation between project leaders’ perspective taking skills and the success of technology implementation processes and to post-hoc evaluations that reveal whether the implementation of the AGV has satisfactorily fulfilled the ex-ante promises. A perceived mismatch between initial expectations and actual observations during the period of usage could have serious practical implications since it is known to violate technology trust [64, 65]. Therefore, a positive attitude and a high level of satisfaction with regard to experienced technology implementation processes should be part of the organization’s goal set. Therefore, we seek to answer the following RQs by incorporating and comparing the attitudes of managerial and operational staff:

  • RQ3a: How satisfied are managers and operational staff with the AGV introduction?

  • RQ3b: How does project leaders’ ability to accurately take the perspective of operational staff affect operational staff’s satisfaction with the implemented AGV?

In line with our particular focus on human factors, we explicitly address managers’ and operational staff’s perceived satisfaction, disregarding quantitative data or key performance indicators (KPIs). Instead of attempting to quantify realized economic potentials, the article sheds light on different perceptions across organizational levels which have considerable implications for adequate technology acceptance and technology usage free from deliberate manipulation behavior. RQ1 addresses the management perspective (see results in Chapter 4.1), RQ2a–c addresses the operational staff level including a comparison between manufacturing and logistic workers (see results in Chapter 4.2), and RQ3a–b compares the views of managerial and operational staff and the particular influence of managers’ perspective-taking abilities (see results in Chapter 4.3).

3 Method and materials

3.1 Participants and design

In total, 89 employees from ten German companies took part in the survey. All companies had already introduced AGVs into a previously existing production environment (so-called brownfield), and all surveyed employees actually share their working environment with AGVs. All were large companies with more than 249 employees in the following industries: automotive (n = 4), electrical (n = 2), packaging (n = 1), sanitary (n = 1), sensors and measurement (n = 1), and service (n = 1). 65 participants were workers (37 in manufacturing, 27 in logistics), 12 project leaders, who were responsible for the AGV introduction in the respective companies, and 12 (higher-level) managers. Participants were aged between 21 and 65 years (M = 41.55, SD = 10.87). Their professional work experience ranged from one to 40 years (M = 16.02, SD = 11.35). Of the participants, just under 14% (n = 12) were female, 83% were male (n = 74), and 1% (n = 1) identified as gender diverse. As Table 1 shows, the number of participants per company varies. For all analyses on the management level, we first calculated average values on the company level based on the assumption that the individual managers’ opinions represent an overarching company strategy. In doing so, we ensured that each company had the same weight in the calculation of overall mean values, regardless of the number of managers who participated in the survey.

Table 1 Number of participants per company and role within the company
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As a result of the survey being conducted in paper-and-pencil style, the sample size slightly varies from question to question, because participants could not be obliged to answer all questions. Additionally, in case of three companies, we were unable to collect answers from representatives of all three roles due to limited access to the field and unforeseen external circumstances in the surveyed companies. Whenever we needed to compare the results from different groups, we excluded these companies from the analysis.

The study is quasi-experimental without any experimental manipulation, since the differentiation into the three groups is based on pre-defined characteristics of the sample, i.e., their role within the company. The survey results are mainly analyzed descriptively, while some research questions required group comparisons by means of inferential statistics.

3.2 Materials and measurements

The used survey encompassed the eight building blocks as visualized in Table 2.

Table 2 Structure of used questionnaire
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In block IV, on the one hand, operational staff rated how relevant they personally consider 17 pre-defined acceptance factors (self-assessment). On the other hand, project leaders were asked to take the perspective of their workers and to estimate how relevant the latter perceive these acceptance factors (external assessment). For each worker, we calculated the average of the absolute values of the discrepancies between the workers’ self-assessment and the corresponding project leaders’ external assessment. This mean absolute error served to operationalize the project leader’s perspective taking accuracy, i.e., his/her ability to understand his/her employees’ perspective. This approach implies that high values would counterintuitively indicate a low perspective taking accuracy, which is why we inverted the sign of the mean absolute errors. Consequently, due to the seven-level relevance scale, the resulting variable perspective taking accuracy can take on values between minus six and zero with the score minus six indicating the lowest possible and the score zero the highest possible perspective taking accuracy. Additionally, we calculated the deviation of perspective taking, which is the average of the actual discrepancies between self-assessment and external assessment. The calculation differed in using positive and negative instead of the absolute values of the discrepancies. The mean deviation of perspective taking allows to identify systematic biases in project leaders’ external assessments.

Like in block IV, the two items in block VII were rated by operational staff in terms of their own opinion and by project leaders in terms of the assumed ratings of their workers.

3.3 Procedure

The survey was conducted from November 2021 to January 2022 in paper-and-pencil style in the companies. Relevant companies were identified by internet research and personal contacts via an AGV manufacturer. Project leaders were contacted by phone or email and asked to display the prepared surveys on the shop floor and encourage their employees to take part. Completed questionnaires could be dropped anonymously into a box set up in advance. Furthermore, project leaders and managers were also asked to fill in the survey.

At the beginning of the survey, the participants received some basic information about the background of the study and data privacy issues. Afterwards, a precise definition of AGV was provided. The proceeding main body of the survey encompassed five thematic blocks (cf. Chapter 3.2 Materials and measurements) with mostly closed and some semi-open questions. After data collection, the handwritten surveys were digitized for the data analysis.

4 Results and discussion

All data analyses were conducted using IBM SPSS Statistics 28. Since data were not normal distributed (Shapiro–Wilk p > .05), we conducted Mann–Whitney U-tests as the non-parametric alternative to unpaired t-tests for group comparisons (see Chapters 3.2 and 3.3). Unless otherwise stated, the distribution of data in the compared groups was similar (Kolmogorov–Smirnov p > .05).

4.1 Management perspective: objectives and success factors (RQ1)

Table 3 shows managers’ and project leaders’ perceived relevance of objectives that companies seek to achieve with the implementation of AGVs.

Table 3 Relevance of objectives that companies seek to achieve when implementing AGVs (n = 12 managers and n = 12 project leaders from ten companies)
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The statistical analysis reveals the multitude of relevant objectives associated with the introduction of AGVs. All objectives were on average considered as at least of partial relevance, indicating that company representatives seem to expect AGVs to lead to a large variety of improvements. The overall most relevant goal was the optimization of material and information flows within the company (M = 6.13, SD = 0.775). Additionally, company representatives expect AGVs to increase productivity (M = 5.89, SD = 0.921), flexibility (M = 5.43, SD = 1.069), and workplace attractiveness (M = 5.43, SD = 1.291), which can be regarded as typical key high-level goals in manufacturing companies [8]. Aspects related to product quality such as a decrease in damages during transport (M = 4.96, SD = 1.052) are considered comparatively less relevant, especially according to the managers’ opinion. However, project leaders who are more deeply involved in the operational business consider this objective as more important (ΔM = 1.12), which hints at managers’ lesser awareness of quality issues and transport damages in day-to-day operations.

The goal of improving the company’s reputation through the implementation of AGVs was given the lowest relevance by company representatives (M = 3.94, SD = 1.258), possibly because related manufacturing and logistics technologies such as autonomous robots or AI applications are generally perceived as more innovative. Notably, managers who are responsible for the overall company strategy and reputation attribute a higher relevance to this objective than project leaders (ΔM = -0.62). In this sense, the discrepancies between managers’ and project leaders’ statements also reflect their responsibilities and their typical strategic versus operative target orientation within the company.

Interestingly, company representatives rated increasing workplace attractiveness (M = 5.43, SD = 1.291) and reducing personal costs (M = 5.23, SD = 1.670) as similarly relevant objectives. The reduction of personnel costs can be achieved by relieving workers from tasks that can be performed by AGVs, and was also mentioned by experts in a previous study in the context of the financial benefits of AGV use [6]. The increasing level of automation causes a relevant proportion of affected workers to fear unemployment [66], one of the most common concerns regarding emerging technologies in intralogistics [1]. The similar relevance ratings of workplace attractiveness and reduction of personal costs repeatedly illustrate the thin line and the area of tension between supporting workers so that their jobs become more appealing on the one hand and substituting workers by technology on the other hand.

The degree to which the above objectives are fulfilled is supposed to depend on several success factors for the AGV project. Table 4 shows success factors and their attributed relevance by managers and project leaders.

Table 4 List of success factors and their relevance rated by managers and project leaders (n = 12 managers and n = 12 project leaders from ten companies)
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Descriptive analysis indicates that company representatives consider the adequate selection of processes to be automated (M = 6.58, SD = 0.498) and ensuring employees’ safety (M = 6.58, SD = 0.746) as most relevant. Further technology-oriented success factors like IT and maintenance support (M = 6.28, SD = 0.427) and a comprehensive analysis and consideration of technical factors (M = 6.28, SD = 0.478) were also considered as highly relevant. These technology-oriented factors are followed by a transparent and appreciative communication of forthcoming changes (M = 6.15, SD = 0.585). Project leaders consider this employee-related factor as even more relevant than managers (ΔM = 0.44). The same applies to the involvement of employees in the project (ΔM = 0.40). Project leaders seem to value these factors which represent key building blocks of well-established CM approaches [47]. Managers, on the other hand, consider well-defined responsibilities and employee support (existence of a process owner, ΔM = −1.17, and support of the employees, ΔM = −0.60) as well as employees’ experience, qualification, and motivation as more relevant than project leaders (internal know-how, ΔM = −0.87, motivation, ΔM = −0.74, and staff training, ΔM = −0.69). Hence, whereas the latter predominantly focus on involving employees from the beginning, managers are more concerned with rather formal aspects such as creating clear responsibility structures and providing employees with adequate training. In conclusion, the gathered data reveals some important differences between managers and project leaders highlighting their partly diverging mind sets and reflecting their areas of responsibility within the organization. Nevertheless, the significance of these findings is limited due to the merely descriptive nature of analysis and to the overall high attributions of relevance for all surveyed success factors.

4.2 Operational staff perspective: acceptance factors, satisfaction, and occupational safety (RQ2a–c)

Table 5 shows operational staff’s opinion on the perceived relevance of certain acceptance factors (RQ2a) and the perceived level of the fulfillment of these factors in the completed AGV implementation project (RQ2b).

Table 5 Perceived relevance and level of fulfillment of acceptance factors rated by operational working staff (n = 64)
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Overall, operational staff perceives many acceptance factors as important but is rather unsatisfied with the fulfillment of these issues. The highest discrepancies are observable in terms of education and training, ΔM = −2.69, SD = 2.061, early involvement in planning and implementation of the project, ΔM = −2.61, SD = 1.928, and transparent communication of change and consideration of concerns, ΔM = −2.57, SD = 1.995. Remarkably, these factors are all so-called soft factors which are independent of the actual technology but represent classic CM issues. In addition, these factors were assigned the overall lowest values for perceived fulfillment and hence represent the main barriers for employee acceptance, indicating an urgent need to improve the quality of CM for AGV implementation processes.

Anthropomorphization of the AGV shows a different pattern in the sense that workers do not consider it as important, but they actually do anthropomorphize the devices once they enter the company, ΔM = 0.63, SD = 2.135. This is in line with previous research on collaborative robots showing an aversion among production workers to admit explicit anthropomorphization while their speech about a collaborative robot indicates implicit anthropomorphization [67]. Furthermore, the standard deviation with regard to anthropomorphization is likewise high, referring to well-documented individual differences in people’s tendency to anthropomorphize technological devices [68, 69].

Notably, both items related to job security have a comparably high standard deviation (SDOwnJob = 2.225, SDColleagues’Job = 2.071), suggesting that there are substantial individual differences among the operational staff in the extent to which they perceive their job to be threatened by the implementation of an AGV.

This heterogeneity underlines the need to explore more deeply the role-specific concerns and attitudes toward technology of affected persons. Therefore, we compared logistics and production workers’ (i) consideration of the AGV introduction as a good idea, (ii) perception of the AGV as some kind of colleague, (iii) satisfaction with the performance of the implemented AVG, and (iv) satisfaction with the planning and realization of the AGV implementation project. As shown in Fig. 1, two-sided Mann–Whitney U-tests revealed that production workers are significantly more satisfied with the performance, U = 291.500, Z = −2.879, p = .004, and agree significantly more with the statement that the AGV introduction was a good idea, U = 291.500, Z = -2.899, p = .004. Descriptively, logistics workers’ ratings on the used seven-level scale show only moderate agreement to the statement that the introduction was a good idea, M = 4.00, SD = 1.179, and that they are satisfied with the AGV performance, M = 3.19, SD = 1.642. In contrast, production workers are more positive about the idea of implementing an AGV, M = 5.35, SD = 1.230, and their performance, M = 4.38, SD = 1.361. No significant differences were found regarding the satisfaction with the planning and implementation, U = 389.500, Z = −1.570, p = .116, and the perception of the AGV as a colleague, U = 392.500, Z = −1.479, p = .139.

Fig. 1
figure 1

Logistics and production workers’ (i) consideration of the AGV introduction as a good idea, (ii) perception of the AGV as some kind of colleague, (iii) satisfaction with the performance of the introduced AVG, and (iv) satisfaction with the planning and realization of the AGV implementation project. Note. Error bars represent standard error of means. * p < .05, ** p < .01, *** p < .001, ns = not significant

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Furthermore, in order to address RQ2c, we analyzed the two acceptance factors related to job security, as we expected logistics workers to be more directly affected by the implementation of an AGV and therefore to perceive it as a greater threat to their jobs. As shown in Fig. 2, operational staff in logistics place a similar high value on job security for themselves, M = 6.62, SD = 0.752, and for their colleagues, M = 6.41, SD = 1.010. Similarly, manufacturing workers also consider the security of their own workplace, M = 6.41, SD = 0.832, and that of their colleagues, M = 6.27, SD = 0.732, to be comparably important. Mann–Whitney U-tests indicate no statistically significant differencesFootnote 1 between both groups in terms of perceived relevance regarding their own job, U = 411.500, Z = −1.149, p = .250, and their colleagues’ jobs, U = 399.000, Z = −1.512, p = .130.

Fig. 2
figure 2

Perceived relevance of own and colleagues’ job security rated by logistics and production workers. Note. Error bars represent standard error of means. * p < .05, ** p < .01, *** p < .001, ns = not significant

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However, regarding the actual perception of job security, two-sided Mann–Whitney U-tests reveal that logistics workers are significantly more concerned about losing their own job than production workers, U = 293.000, Z = -2.876, p = .004 (see Fig. 3). While the values for operational working staff in logistics are in the middle of the seven-point scale, M = 3.78, SD = 2.082, the values for production workers are considerably lower, M = 2.32, SD = 1.334, suggesting little concern about losing their own jobs due to the implementation of the AGV. No significant differences were found regarding concerns of colleagues losing their job, U = 370.000, Z = −1.789, p = .074.

Fig. 3
figure 3

Perceived concerns regarding one's own and colleagues' job security rated by logistics and production workers. Note. Error bars represent standard error of means. * p < .05, ** p < .01, *** p < .001, ns = not significant

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In conclusion, the perception of logistics and production workers differed significantly as assumed (see Chapter 1.2), supposedly due to the varying ways these workers relate to the AGVs. Whereas logistics workers consider AGVs as a major threat to their jobs, which reduces their perceived job security, production workers indeed have to deal with the AGVs and their trajectories within their daily work, but do not see the AGVs taking over their typical working tasks. In line with previous studies [8, 46], this confirms that human-technology relationships and fears of job loss have a significant impact on the attitude of operational staff towards modern technology and should definitely be taken into account in technology introduction processes.

4.3 Deviating perspectives and perspective-taking (RQ3a–b)

Regarding RQ3a, as shown in Fig. 4, the two-sided Mann–Whitney U-tests revealed that project leaders and managers are significantly more satisfied with the performance of the implemented AGV, U = 495.000, Z = −2.687, p = .007, as well as with the planning and implementation process, U = 468.000, Z = −2.986, p = .003, compared to the operational staff. While the operational staff was on average neither dissatisfied nor satisfied with the performance, M = 3.85, SD = 1.593, and the planning and implementation, M = 3.65, SD = 1.473, as suggested by scores fairly in the middle of the seven-level scale, project leaders’ and managers’ assessments were more positive regarding the performance, M = 4.88, SDPerform = 1.361, as well as the planning and implementation, M = 4.71, SD = 1.459, but still far away from being satisfied or even very satisfied.

Fig. 4
figure 4

Satisfaction rating of operative working staff and project leaders and managers on the performance and the planning and implementation of the AGV. Note. Error bars represent standard error of means. * p < .05, ** p < .01, *** p < .001, ns = not significant

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Table 6 provides further details on project leaders’ perspective taking accuracy and the mean perspective taking deviation per acceptance factor (cf. Chapter 3.2). In case of a low perspective taking accuracy, a mean deviation far above or below zero indicates that project leaders’ systematically over- or underestimated workers’ perceived relevance of a factor, whereas a mean deviation close to zero indicates no systematic bias in project leaders’ perspective taking. Interestingly, project leaders had the most difficulty in estimating how relevant their employees perceive the anthropomorphization of the AGV, as indicated by a perspective taking accuracy of M = −1.75. This means that, on average, their assessment differed by almost two levels on the used seven-level scale. The calculated mean deviation reveals that they systematically overestimate the relevance their employees attach to the factor anthropomorphization of the AGV by M = 0.70. This is consistent with the finding that the operational staff’s self-assessment and the management’s external assessment as to whether the workers perceive the AGV as some kind of colleague differs significantly, U = 544.500, Z = -2.208, p = .027. The observed differences can be interpreted in two plausible ways. Either managers misjudge operational staff’s perception, or operational staff’s self-assessment does not perfectly reflect their actual perception. Previous research on the related phenomenon of anthropomorphism provides empirical support for the second hypothesis. Anthropomorphism describes human’s tendency to mentally ascribe human characteristics to a machine [70], which can indeed be considered as a necessary prerequisite for perceiving something as a (human-like) colleague instead of a tool. In this context, the so-called model of dual anthropomorphism posits that actual anthropomorphization is difficult to detect through self-assessment questionnaires, as these cause reflective cognitive processes to interfere with a subliminal tendency to anthropomorphize, thereby overshadowing implicit anthropomorphism [71]. Previous exploratory and mostly single-case studies have also revealed that anthropomorphization occurs with respect to other types of robots [46, 72-74]. However, in line with the model of dual anthropomorphism, factory workers have found to use anthropomorphic descriptions while at the same time refusing to consider anthropomorphization as a relevant issue in production [67].

Table 6 Mean perspective taking accuracy and mean deviation in perspective taking in terms of perceived relevance of acceptance factors. Note. A higher perspective taking accuracy is indicated by less negative values with zero indicating perfect accuracy
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Furthermore, project leaders seem to systematically underestimate how important their employees perceive the general benefit for companies and for the employees, M = −0.71, and also struggle to accurately assess how relevant their employees perceive their personal benefits associated with AGV introduction, M = −1.33. Project leaders may underestimate that altruistic rather than egoistic goals also play a decisive role in the acceptance of a technology by the employees. Similarly, project leaders seem to be insufficiently aware of their employees’ need for education and training prior to the implementation of an AGV, as indicated by a mean deviation in perspective taking accuracy of M = −0.41.

Project leaders’ perspective taking accuracy was highest in terms of safety, M = −0.51, and job security, M = −0.52, which represent the most relevant acceptance factors from the operational staff’s viewpoint (cf. Chapter 4.2). While this shared view on the most important aspects is clearly beneficial for employee acceptance, the analysis also revealed some systematic biases and notable difficulties for project leaders to accurately capture the perspective of their employees, which is likely to lead to suboptimal prioritization of certain aspects during the AGV implementation process and hinder employee acceptance.

The findings presented so far suggest that project leaders sometimes fail to accurately predict their employees’ perspectives. However, the consequences of this inaccuracy remain unclear. In order to shed light on this possible relationship between project leaders’ ability to predict their employees’ attitude and their satisfaction (RQ3b), we calculated Pearson correlations between these two variables.

As can be seen in Table 7, we found moderately strong significant correlations, according to [75], between the calculated perspective taking accuracy and (i) employees’ satisfaction with the planning and realization, r(61) = .314, p = .012, and (ii) employees’ satisfaction with the performance of the AGV, r(61) = .326, p = .009, as well as weak significant correlations between perspective taking accuracy and employees' consideration of the AGV introduction as a good idea, r(61) = .267, p = .035.

Table 7 Correlations between project leaders’ perspective taking accuracy and different items of employees’ satisfaction
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The significant correlations between employees’ evaluations and project leaders' perspective taking accuracy are particularly striking. They suggest that project leaders’ accurate understanding of their employees’ perspectives is indeed a crucial driver for employee satisfaction and technology acceptance. From a practical viewpoint, an accurate and detailed understanding enables managers to take into account the desires and needs of their employees during the introduction process, thus fulfilling a basic requirement for successful CM and employee acceptance [55]. Of course, this does not guarantee that a project will be considered successful from a management perspective, nor that the associated goals will be achieved. Nevertheless, managers considered employees’ technology acceptance to be one of the most important drivers of project success. Consequently, in line with previous research [60, 61], managers should highly welcome project leaders’ perspective taking skills in order to foster a cooperative atmosphere and overcome predominantly egocentric considerations. This is rational from an instrumental and egoistic stance as it could promote project success, and also from a human-centric and altruistic stance in a sense that it takes into account the ethical imperative not to ignore the desires of affected persons [61]. Nevertheless, project leaders may not be sufficiently motivated to take into account their employees’ attitudes and demands, perhaps because they do not consider it necessary or beneficial, or because they are convinced that they know better what is best for their employees and/or for the company, or simply because they feel that they already know their employees well enough so that they could save the cognitive capacity required for active perspective taking [61]. Similarly, hierarchical power has been associated with a lower likelihood for active perspective taking [76]. In summary, although an in-depth analysis of the multitude of antecedents, mediating factors, and also possible negative outcomes of perspective taking were beyond the scope of this piece of empirical field research [cf. 60 for a more extensive review], the outlined findings nevertheless have important implications for project management initiatives and go beyond the introduction of AGVs or similar technology.

5 Limitations

The present study is not without limitations. Firstly, the sample is biased in the sense that the sampling procedure was selective, i.e., we did not ask the entire population to participate. As a result, companies and employees with a higher willingness to support research are overrepresented in the sample. In particular, companies where the implementation of AGVs had failed or where AGVs had been implemented as part of the construction of a completely new production environment (greenfield) were not included in the sampling procedure. As a result, it is likely that the overall satisfaction and employee acceptance in the field with regard to AGV implementation is even lower than reported in this study.

Secondly, as there was no experimenter present when participants completed the surveys, we reduced the risk of experimenter biases on the one hand but could not control that all questions were answered thoroughly on the other hand. Additionally, we cannot rule out the possibility that subjects may have participated more than once. However, we consider the likelihood of such misbehavior to be low, as workers receive no compensation or reward for their participation.

Thirdly, this study focused on the perception of employees and did not include objective measures about project success and the performance of the implemented AGVs. However, it would be interesting to see to what extent the satisfaction of operational staff correlates with objective key performance indicators. Future research should take this into account.

Fourthly, regarding our analysis on project leaders’ perspective taking ability, the results are limited in the sense that we explicitly asked the project leaders to take the perspective of their employees and to assess their perceived relevance of certain acceptance factors. Therefore, we have no information on their willingness to take, respect and include the perspective of their employees in their decisions. However, from a practical viewpoint, these two aspects represent necessary prerequisites for perspective taking accuracy to have practical implications. Therefore, they should be considered in further research.

6 Conclusion

In this article, we focussed on AGV as a relevant technology particularly for intralogistics, which has indeed a long tradition but is currently experiencing another wave of euphoria. By conceptualizing AGVs as an interactive technology that changes the working routines of logistics and manufacturing workers, we have highlighted the complexity and the relevance of employee acceptance and satisfaction. Although related research regularly identifies issues such as a lack of employee acceptance as critical barriers to AGV adoption and reports cases of deliberate manipulation of AGVs leading to dysfunctionality and efficiency losses, research lacks comprehensive and empirically based frameworks that capture the factors that determine the success of an AGV implementation. Furthermore, from a project management perspective, AGV implementation processes require the involvement of company representatives from different levels within the organizational structure, namely, managers, project leaders, and operational staff. As previous research on perspective taking has shown, the representatives at different levels need to work closely together and understand their respective views on the technology implementation. While managers may focus primarily on economic benefits, project leaders may concentrate on production processes, and operational staff on their work routines and job security, leading to different perspectives on AGVs. Therefore, in addition to developing a clearer picture of the key objectives, success factors, and acceptance factors in the AGV context, this article contributes to existing research by contrasting the attitudes of employees at different hierarchy levels as well as by investigating the impact of project leaders’ perspective taking accuracy on employee acceptance and satisfaction. To this end, we carried out a field study in order to survey staff on different levels in ten German companies which have lately introduced AGVs in their production environment.

Results reveal relevant differences in the perspectives of high-level managers, project leaders, and operational staff on success and acceptance factors as well as in their satisfaction with the implementation of the AGVs. Additionally, at the operational level, perspectives also differ between manufacturing and logistics workers. The latter are more sceptical about the AGV implementation because, unlike manufacturing workers, they see themselves in a competitive relation to the AGVs, which are perceived as a threat to their jobs. Strikingly, project leaders’ perspective taking accuracy is found to have a significant impact on operational staff’s satisfaction. This underlines the importance of perspective taking as a facilitator for employee satisfaction and acceptance in overcoming these major obstacles for AGV implementation in practice (cf. Chapter 2.2). The empirical findings have manifold practical as well as theoretical implications. On the one hand, responsible company representatives can use the results to raise awareness of crucial success and acceptance factors, to take them into account when planning an AGV implementation, and to address them during the execution. On the other hand, the results enrich the scarce literature base on perspective taking in organizational contexts by providing empirical support for the hypothesis that perspective taking accuracy significantly affects employee satisfaction. As this is, to the best of our knowledge, the first study to provide empirical support for this relationship, further field research that strengthens the empirical base and incorporates additional factors covering the organizational context is urgently needed. Further research could also strive to link the present findings to objective indicators for project success in order to bridge the gap between subjective and objective assessments of project success.