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Purpose – The purpose of this paper is to identify the variables that inﬂuence corporate real estate (CRE) decision-making and gauge their relative importance to each other, thereby understanding the consequent challenges/implications for CRE managers (CREM’s). Design/methodology/approach – Interviews were undertaken with experienced CREM’s using the causal network elicitation technique to create decision networks for the variables they considered for the speciﬁcally deﬁned scenario: dealing with surplus property from a change of business strategy. These networks illustrate the complexity of the mental representations required for the realignment of the CRE portfolio. The key variables are more extensive than alignment theory suggests, namely, ﬁnancial stakeholders. Additional variables identiﬁed include risk, lease accounting, costs, ﬁnancial analysis, business metrics and motivational drivers. The latter indicates the importance of self-esteem and peer recognition for CREM’sand ﬁnancial beneﬁts for the C-suite. Accordingly strategy alignment needs to incorporate CRE both in terms of strategy creation and implementation. Findings – These networks illustrate the complexity of the mental representations required for the realignment of the CRE portfolio. The key variables are more extensive than alignment theory suggests, namely, ﬁnancial stakeholders. Additional variables identiﬁed include risk, lease accounting, costs, ﬁnancial analysis, business metrics and motivational drivers. The latter indicates the importance of self-esteem and peer recognition for CREM’s and ﬁnancial beneﬁts for the C-suite. Accordingly, strategy alignment needs to incorporate CRE both in terms of strategy creation and implementation. Originality/value – This research appears to be the ﬁrst that looks in detail at the mental representations used by decision-makers while making CRE decisions. Keywords Decision-making, Alignment, Corporate real estate, Decision networks, Mental representations Paper type Research paper 1. Introduction Business strategy reﬂects the environment a business operates in, previously it was relatively stable but has become ﬂuid and less predictable. The default business strategy of sustained © Howard Cooke, Rianne Appel-Meulenbroek and Theo Arentz. Published by Emerald Publishing Journal of European Real Estate Research Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and pp. 100-119 Emerald Publishing Limited non-commercial purposes), subject to full attribution to the original publication and authors. The full 1753-9269 DOI 10.1108/JERER-05-2020-0029 termsof thislicence may beseen at http://creativecommons.org/licences/by/4.0/legalcode competitive advantage (Porter, 1985) appears to be losing its relevance. The environment is Corporate real subject to turbulence and unpredictability (Ramirez and Wilkinson, 2016), consequently estate decision- business strategy frequently requires radical adjustment. Strategic agility comprises both the making strategy content and its implementation, the latter necessitating resource agility to facilitate the recalibration of commitments (Doz and Kosonen, 2010). A dynamic resource capability and the ability to implement change affects the performance of the ﬁrm (Barreto, 2010). Corporate real estate (CRE), property that a ﬁrm occupies for its own purposes, is frequently the second largest cost and resource and has the potential to signiﬁcantly impact competitiveness. Cooke et al. (2019a) identiﬁed that in 2014 for a FTSE350 company the mean CRE rent commitment was £544m, freehold ownership (book value) was £797m and mean proﬁts were £475m. Despite its importance CRE is generally regarded as a niche technical subject not meriting mention in management literature (Lizieri, 2003), limited board attention (Apgar, 2009) and it continues to be viewed as a ﬁxed operational cost to be minimised (Stadlhofer, 2010). However, strategic agility requires CRE to have the capability for dynamic alignment but there appears to be a “disconnect between the CRE and business strategy” (Cooke et al., 2019a, p. 184) as the existence of surplus leasehold property indicates a lack of dynamic alignment capability (Cooke et al., 2019b). CRE researchers have not identiﬁed a common alignment deﬁnition (Heywood and Arkesteijn, 2017). The deﬁnition adopted is to apply CRE “in an appropriate and timely way and in harmony with business strategies, goals, and needs” (Luftman and Brier, 1999, p. 109). Achieving dynamic resource capability and implementing a new strategy necessitates a series of decisions. The importance of CRE decision-making has been recognised in several papers because “to add value to the ﬁrm, CRE manager (CREM) decision-making must be linked to the strategic decision-making level of the organisation” (Lindholm et al.,2006,p. 468). However, research on CRE decision-making has been limited to speciﬁcissues. CRE alignment research has focussed on creating higher, strategic level models (Heywood and Arkesteijn, 2017 for a comparison of 20 different models). There is a paucity of research examining CRE decision-making processes for reaching alignment and its relationship with business metrics. This knowledge gap includes the CRE decision-making process itself and the attributes and beneﬁts that are sought by the decision-maker when making individual decisions. Such research is necessary to ﬁll in “block 4” of Heywood and Arkesteijn’s(2018) overview of necessary components for CRE alignment models: ‘actioning the real estate intervention and required CREM practices. This study seeks to investigate the mental representations (MRs) of CRE decision-makers and how different alignment decisions can arise from one problem. It does so by taking a behavioural approach and conducting semi- structured interviews with decision-makers using the causal network elicitation technique (CNET) (Arentze et al.,2008) to determine their considerations and preferences. CNET uses decision networks, an extension of Bayesian belief network methodology, to make inferences by placing variables in “causal” networks to represent decisions. This paper adds to the body of knowledge within both business and CRE research by examining the “black box” of CRE decision-making. It does so by focussing on how decisions are made when changing the CRE portfolio and seeks to identify and assess the relative importance of those variables that inﬂuence CRE decision-making. Its results will help in the understanding of the drivers of CRE decision-making; provide the C-suite (e.g. CEO) with an understanding of CRE complexity; and offer insights into CRE decision-making and realignment. Consequently, ﬁrms will be better placed at achieving strategic alignment. Decision-making is a complex process. To unravel the complexity, “lifting the lid” on a decision-making process, requires a focus on a speciﬁc issue. Therefore, the goal of this study is to investigate decision-makers MR’s when faced with the problem of a changing business strategy requiring portfolio realignment to reduce the CRE commitment. The JERER research questions are: 14,1 Q1a. What decision variables are identiﬁed by the CREM in their mental representation of the problem, together with the attributes, beneﬁts and situational variables associated with those decisions? 102 Q1b. What is the relative importance of the decision, attribute, beneﬁt and situational variables? Q2. What are the links between decision, attribute and beneﬁt variables in such a scenario? (Situational variables are not included because they are not affected by the decision.) The remainder of the paper is structured as follows. Section 2 discusses decision- and speciﬁc CRE decision-making interventions, followed by the description of the methodology. In Section 4, the results are presented, followed by conclusions and discussions ending with limitations and suggestions for further research. 2. Literature review The underlying business strategy of sustainable competitive advantage (Porter, 1985) has been increasingly challenged by alternative, more ﬂuid, dynamic strategies, including transient competitive advantage (McGrath, 2013). Organisations that espouse these new strategies seek to be unfettered by assets that can become liabilities and require systems with change embedded as a core value. The new strategy playbook is continuously changing, emphasising lifecycle phases, blending stability with dynamism and balancing a stable vision with variety in its execution (McGrath, 2013). More ﬂuid strategies are of shorter duration entailing greater resource ﬂexibility. This requires ﬂexible CRE to support and facilitate not hinder change, thereby requiring a portfolio of short-term leases or serviced space. This increases costs but is not an option for all sectors (e.g. manufacturing). CRE research has focussed on tangible aspects of property, namely, the physical environment. However, intangible aspects (e.g. CREM practices and decision-making) are hard to replicate which provides an advantage (Heywood and Kenley, 2008). What is clear is that “to meet the challenges businesses are facing today ﬁrms need efﬁcient and productive work environments with physical, functional and ﬁnancial ﬂexibility” (Lindholm and Leväinen, 2006,p.39). A ﬁrm will seek to improve its CRE because corporate proﬁtability depends on meeting performance criteria (Van Ree, 2002). The key elements of CRE strategy implementation are asset management, new space acquisition and surplus space disposal (Kämpf-Dern and Pfnür, 2014). To be effective this requires a dynamic alignment capability, the ability to adjust CRE. That appeared to be lacking during the Great Recession (UK 2007 to 2014) when despite the business volatility companies expanded their CRE (Cooke et al., 2019a). A complex world and limited brain processing capability means rational decision- making requires effort. Consequently, individuals use mental structures to organise knowledge (Mintzberg et al.,2009), where the resulting schemas (mental maps) represent knowledge at different levels. Mental short-cuts (heuristics) that look to simplify the world can lead to over-simpliﬁcation and distortions. The iterative process of learning from experience is a messy unpredictable one, often creating conﬂicting outputs as, “people consistently act inconsistently” (Argyris, 1991, p. 4). Decision-making requires a multi-level perspective, an iterative, innovative process (Chiva et al.,2014) with agility to integrate knowledge and produce a range of products/services (Junni et al.,2015. Organisational decisions are not stand-alone but have many different linkages that increase complexity. CRE decision-making research has primarily focussed on the acquisition decision, Corporate real including inﬂuence factors (Nunnington and Haynes, 2011), location choice (Rothe et al., estate decision- 2015) and acquisition (Greenhalgh, 2008). Research suggests small businesses decisions are making made by individuals, whereas in larger ﬁrms the process is multi-facetted multi-person and more objective (Mazzoral and Choo, 2003). However, “regardless of ﬁrm size is that the behaviour, personal preferences, priorities and perceptions of key individuals will inﬂuence the ﬁnal outcome” (Greenhalgh, 2008, p. 123). There has been research on CRE alignment with corporate strategy (Haynes, 2012; Gibler and Lindholm, 2012) and consideration of how CREM’s can “add value” (Jensen et al.,2012), but lacking is the overall CRE decision-making process, which is surprising because, “effective real estate decisions are integral to the realisation of overall business objectives” (Nourse and Roulac, 1993, p. 476). The disposal of surplus property is disregarded despite its importance to CRE strategy implementation (Kämpf-Dern and Pfnür, 2014) and its substantial increase through the Great Recession (Cooke et al., 2019a). It remains unclear what beneﬁts CRE Managers seek when making decisions around surplus property and which attributes are expected to lead to those beneﬁts. The research gap suggests there is still a long way to go before a widely agreed holistic CRE management system is deﬁned “that enables decision-makers to make the right choices” (Riratanaphong and van der Voordt, 2015, p. 789) and see the whole picture around CRE decisions. There has been extensive research on decision-making (Kahneman, 2011). Decisions require a process of evaluation on the possible courses of action, the reoccurrence of events allows previous experience and decisions to create choice preferences. In new situations those prior experiences and decisions cannot provide an immediate solution. Instead an active mental process is required to explore and evaluate possible solutions. MR theory states that individuals will construct a mental model to assess the consequences of possible actions (Johnson-Laird, 1983). The MR will be a simpliﬁcation of reality subject to the individual’s bias but will incorporate all variables because working memory limitations. Arentze et al. (2008) and Dellaert et al. (2008) consider how MRs of decision problems are structured and the components are illustrated in Figure 1. Situation elements are outside the control of the individual (e.g. the environment), whereas the individual needs are the basic values of the individual (e.g. goal attainment) and tend to be relatively stable. Within the MR decision alternatives are possible options that ﬂow from the alternative actions. The causal network is the active component of the MR by which an individual can assess the likely Figure 1. Mental representation: components of a decision problem outcomes and represents their knowledge of the problem. Finally, the values assigned by the JERER individual to the possible states are the utility weights. 14,1 The second stage is the classiﬁcation of the decision alternatives and the delineation of the different variables, as illustrated in Figure 2. Decision variables are elements that the decision-maker can choose (e.g. close an operating property) with a yes/no answer. External factors beyond the control of the decision-maker (e.g. the property market) are classed as situational variables. There are two categories of outcome variables, the consequences of decisions made. An attribute is the directly observable part of the system (e.g. balance sheet impact). A beneﬁt is more abstract and closer to the needs of the individual decision-maker (e.g. reduced CRE costs). A methodological challenge is ensuring that the beneﬁt identiﬁed is the ﬁnal one; the “end value” from the exercise and it offers sufﬁcient explanation of the individual’s motivation. The utility is the value assigned by the individual to the beneﬁt. The MR generated by the process will be based on the causal relationship between the attributes and the beneﬁts. 3. Methodology The interview was selected as the appropriate technique to obtain data, as it provides the opportunity to look deeply inside the motivations of individuals and explore their MR’s. Invitations to participate were sent by email to experts (senior British CREM’s) known to the ﬁrst author in different organisations, setting out the nature of the request and what was entailed. The background of the experts covers a range of business sectors, numerous property types and individually over 15 years’ experience in a range of organisations. The scenario posed and selection of the experts were deliberately non-sector speciﬁc. It was Figure 2. Classiﬁcation of decision problem variables and directions of causal relationships anticipated that there would be commonality in many areas, but certain variables identiﬁed Corporate real might be sector speciﬁc, which was the case. Nine interviews took place in spring 2019. estate decision- Each interview commenced with setting out the structure of the interview and follow up making input. The realignment scenario posed was: The business environment in which your company operates has changed leading to the adoption of a new business strategy. Along with the longer-term trend to use space more eﬃciently there is a need to signiﬁcantly reduce the CRE commitment in the short term. Eﬃciency relates to both square metres and annual cost. Consequently, the leased part of the CRE portfolio needs to be realigned to meet these new requirements. The choice to focus the research on the leasehold part of the portfolio was based on the assumption that a ﬁrm will tend to own space that is core to its needs, whereas leased space is peripheral and more likely to be reduced with a change of environment (Gibson, 2000). If closure of a property cannot coincide with a lease termination event surplus leasehold space is created. Surplus leasehold space is generally more complicated than owned surplus space to dispose of because lease constraints need to be dealt with. The narrow scenario parameters provide focus for the decision-makers to describe how they consciously or sub- consciously construct MRs of the decision problem and evaluate the alternative courses of action. The scenario highlights the business’s strategic choice and the decision-makers role is to respond by dealing with the CRE portfolio at the tactical level. In this paper, the focus will be on the homogeneity of the links and hence on the commonality of the experts decision-making considerations. The methodology asked each expert about their decision-making, not how their current organisation makes decisions. We did not focus on the organisation for two reasons. Firstly, it would remove any constraints of organisational protocols and potentially perceived market sensitive information. Secondly, the expert could apply all of their experience to the situation rather than provide ﬁrm or sector speciﬁc views. This way would seek to elicit as much information as possible on decision-making processes and the MRs adopted. The technique for acquiring the information to model the MR of the decision-makers is CNET, a semi-structured interview method in which the expert is presented with a decision problem (Arentze et al., 2008 for further explanation, also of validity and reliability of this method). Throughout the process the interviewer needs to elicit information from the expert but avoid interfering with their thought process. It seeks to get the expert to think aloud making the task of the interviewer that of an interpreter of what is mentioned classifying the variables and comments. The CNET stages adopted in this paper, in line with MR theory, are as follows: Stage 1: Decision variables – the expert is asked, “what are the possible courses of action you see in this scenario?” This seeks to identify the decision variables. In this stage, the expert was kept focussed on the decision alternatives rather than allowed to develop considerations. Stage 2: Situational variables and attributes – the expert is then asked in relation to each decision variable identiﬁed, “assume you have to make a decision what are your considerations when faced with the problem?” Throughout the process the interviewer distinguished between situational variables, those that are outside the control of the decision-maker (e.g. the current property market) and attribute variables, which are a consequence of the decision (e.g. creating a surplus property portfolio). If a beneﬁt is stated (e.g. reduced cost to expiry) rather than an attribute, the question asked is “how is this beneﬁt reached?” Stage 3: Beneﬁts – for each attribute variable identiﬁed the beneﬁts associated with it were sought by asking “why is this variable important?”, the objective being to reach the “end value” and ensure that there are no subsequent beneﬁts that ﬂow from the attribute. In JERER the Causal Network there may be multiple links from an individual attribute to beneﬁts. 14,1 Each interview was recorded and transcribed and from which a network of the situation, decision, attribute and beneﬁt variables was created in the software programme Lucidchart. The network identiﬁed which beneﬁts are considered and how one individual beneﬁt may be inﬂuenced by several different attributes. The causal network structure imposes constraints on possible links. As a beneﬁt is a consequence of an attribute, it cannot link to an attribute. Decision and situational variables cannot have incoming links, the former because they are choice variables and the latter because they are an external factor. Both generally link to beneﬁts via attributes, but can link directly to beneﬁts too. The nine interviews produced a total of 39 situational, 75 decision, 472 attribute and 136 beneﬁt variables. The variables were categorised to provide consistency across the interviews and then tabulated with only one score per variable per expert to give a maximum variable score of n =9 (n = number of experts). Individual variables were grouped under the headings CRE, Finance (FIN), Human Resources (HR) and Management (MAN). For each group we calculated the mean (X=mean of the group), which is the number of mentions divided by the individual variables in that category. The score for each variable (n) indicates the agreement across the experts about a particular variable. The group means (X) indicates the probability that it is a CRE, FIN, HR or MAN variable: the higher X the greater the centrality of views by the experts. For each expert a matrix was produced to record each decision-attribute and attribute- beneﬁt link. The individual matrices were combined to provide one aggregated decision- attribute and one attribute-beneﬁt matrix. The average of the attribute for each decision and each beneﬁt was calculated by dividing by the number of experts (nine). This provides the degree of centrality for the ingoing or outgoing links. Comments from individual experts are referenced E1, E2, etc. 4. Results and discussion 4.1 Question 1: types of variables After categorisation, 7 situational, 4 decision, 29 attribute and 19 beneﬁt variables were identiﬁed. Table 1 identiﬁes the occurrences for each variable across the nine experts. The last column in the tables show the number of experts who identiﬁed that variable. The mean is the average number of times a particular variable in that group was mentioned. 4.1.1 Decision variables. Three decision variables were uniformly identiﬁed by all the experts (n = 9). Landlord deal comprises surrendering or restructuring the lease, both require the acquiescence of the landlord. Secondly, lease events include decisions that break a lease contract and those that operate the lease expiry, both are speciﬁed in the lease. Thirdly, third party deals refer to a decision to dispose of the property to an external party by lease assignment, subletting, or portfolio transaction. The fourth decision variable category of space utilisation (n = 6) involves actions not requiring third-party involvement and aims to mitigate costs and/or maximise use (e.g. reconﬁguration) of space. All of the decisions are well-established approaches to realignment. The ﬁrst three variables change the portfolio composition, whereas space utilisation aims to optimise the use of retained space. The interviews identiﬁed that the C-suite sets both the business and CRE strategies. The CREM presents opportunities to the C-suite (which make the decision) and then implements the decisions, making them an “enabling function” (E7). Tactical decisions during implementation may add value, for example by negotiating speciﬁc lease terms, but “ultimately, the decision sits with the business” (E8). The literature on alignment assumes Corporate real Variable group Variable Frequency estate decision- Decision variable making CRE Landlord Deal 9 CRE Lease Event 9 CRE Third Party Deal 9 CRE Space Utilisation 6 Decision Total 33 107 Decision Mean 8.25 Situational variable MAN Business Strategy 9 CRE Data 9 CRE Property Market 8 MAN Business Environment 4 CRE Landlord 4 MAN Corporate Solution 2 MAN Supply Chain 1 Situational Total 37 Situational Mean 5.29 CRE Situational Mean 7.00 MAN Situational Mean 4.00 Attribute variable CRE Lease Detail 9 FIN Cost 9 FIN IFRS16 9 MAN Risk 9 CRE CRE Designation 8 CRE CRE Tactics 8 CRE Proposed Portfolio 8 CRE Space utilisation 8 FIN Business Metrics 8 FIN Financial Analysis 8 CRE Prop Mgt 7 CRE Restructure 7 CRE Workplace Strategy 7 MAN Decision Making 7 CRE CRE Metrics 6 CRE Current Portfolio 6 CRE Implementation 6 FIN CAPEX 6 FIN Cash 6 HR HR Proﬁle 6 MAN Corporate Governance 6 MAN Operational Issues 6 MAN Stakeholders - External 6 CRE Landlord 5 MAN Business Strategy 5 MAN Stakeholders - Internal 5 HR HR Practices 4 Table 1. HR Motivation 2 Frequency of CRE CRE Market Risk 1 Attribute Total 188 occurrence in MR’sof Attribute Mean 6.48 decision-makers of CRE Attribute Mean 6.62 each variable across (continued) nine interviews JERER Variable group Variable Frequency 14,1 FIN Attribute Mean 7.67 HR Attribute Mean 4.00 MAN Attribute Mean 6.29 Beneﬁt variable CRE Implementation Beneﬁts 8 MAN Financial Stakeholders 8 MAN Risk Reduction 7 CRE CRE Proﬁle 6 MAN Operational Beneﬁts 6 FIN Cost Reduction 5 HR HR Beneﬁts 5 HR Motivational Drivers 5 MAN Stewardship 5 CRE CRE Future Prooﬁng 4 FIN Business Metrics 4 CRE Proposed Portfolio 3 FIN Proﬁt and Loss 3 HR HR Talent 3 CRE CRE Metric Improvements 2 FIN CAPEX 2 FIN Cash 2 MAN Business Prospects 2 CRE Prop Mgt 2 Beneﬁt Total 82 Beneﬁt Mean 4.32 CRE Beneﬁt Mean 4.17 FIN Beneﬁt Mean 3.20 HR Beneﬁt Mean 4.33 MAN Beneﬁt Mean 4.67 Table 1. that CRE decision-making is undertaken by the CREM (Gibler and Lindholm, 2012). This research suggests that, certainly in the present sample, this is not the case, only tactical decisions are made by the CREM. 4.1.2 Situational variables. Business strategy (n = 9) was identiﬁed by all experts as a situational variable because it determines CRE strategy and the CREM needs to achieve clarity on it, as “it is pretty rare in my experience that people’sbusinessstrategy isfairly coherently deﬁned” (E1). An adjunct to that is the Business Environment (n =4), which inﬂuences future business strategy and, therefore, CRE strategy. Data (n = 9) is information on the existing CRE portfolio and people in the organisation. Data forms the starting point of CRE decisions because “contractual obligations are your baseline” (E3), from which the CREM will be able to identify units that can be readily exited and/or will become surplus if designated “non-operational”.It is not just data per se that was deemed important, but knowledge of the CRE portfolio data is extremely valuable (E7) together with the ability to apply it to a given circumstance. The property market (n =8) inﬂuences decisions about property disposability, while landlord proﬁle (n =4) inﬂuences how the landlord will affect a proposed solution. Of the seven situational variables, four were MAN (X = 4.0) and three CRE (X = 7.0). The key situational variables are those that relate to the business decision necessitating the realignment (business strategy and its environment), together with factors that impinge Corporate real upon the realignment process, the portfolio itself and the property market. estate decision- 4.1.3 Attribute variables. Cost related attributes comprise Cost (n = 9), CAPEX (capital making expenditure) (n = 6) and Cash (n = 6). Surplus property has on-going costs (e.g. rent), new costs relating to the vacancy (e.g. security) plus capital costs from disposal decisions (e.g. a premium). The experts distinguished between the immediate cash effects of change and annual reported costs, the latter being an important consideration, including IFRS16 (n =9), business metrics (n = 8) and ﬁnancial analysis (n = 8). Financial reporting is important because it is what external parties (shareholders and the ﬁnancial markets) see when st assessing company value. IFRS16 changed lease accounting from 1 January 2019 to provide visibility of total lease costs and is anticipated to result in shorter lease terms plus a change in the CRE proﬁle within the C-suite as, “the door to the C-suite is opening because of property liabilities going on balance sheet” (E7). In summary, the ﬁnancial implications of decisions are important as they have a direct relevance to the current value of the business through the share price. Risk (n = 9) was highlighted in the interviews, as “you always need to look at your least risk scenario” (E3). Risk takes a number of forms including the overall business risk (e.g. CRE inﬂexibility) and contingent risk (e.g. assignment). Collectively the approach of the experts was to select the least risky option and maximise CRE ﬂexibility, as ask: [.. .] any property director how much ﬂexibility they would like they will always say they want as much as they can. In reality ﬂexibility costs money, real money in terms of premium rents or lack of discounted rents (E5). Business strategy is very important as a situational variable (n = 9) but less so as an attribute (n = 5). It is “imposed” on the CRE Team and forms the cornerstone of CRE realignment. As an attribute it is more about the change process and the cycle of business models, which is iterative in development and implementation, because “we know that as businesses evolve their operational needs will change” (E5). External stakeholders (n = 6) primarily concerns shareholders, as “you always want to keep shareholders happy” (E4), but includes the broader investor marketplace, together with customers. Internal shareholders (n = 5) includes senior management and colleagues impacted by CRE realignment. Ensuring correct procedures and compliance is identiﬁed in the MR’s through decision-making (n = 7) and corporate governance (n = 6). Some experts highlighted the difference between quoted and private companies, the former requiring stricter procedures, company stewardship and internal dispute resolution. Core business efﬁciency, operational business unit requirements and whether different business units can share space are also considerations in the MR’s (operational issues, n =6). CRE attributes can be grouped into ﬁve categories: current CRE, measuring CRE, what CRE is wanted, how to achieve the CRE wanted and consequences of achieving the required portfolio. Current CRE comprises lease details (n = 9) (e.g. break dates) and the current portfolio (n = 6). The latter refers to the portfolio’s capability for dynamic alignment to match the portfolio to the new business strategy (E1). CRE metrics (n = 6) refers to performance and on-going suitability of space for operational use, which assists in the selection of which unit(s) to close. What is wanted (CRE Designation, n = 8 and Proposed Portfolio, n =8) encompasses change to the core-peripheral property balance (Gibson, 2000), future CRE ﬂexibility and specialist property requirements. CRE realignment was identiﬁed as a continual process, as “inevitably the business environments change, strategies change, [.. .]. therefore, the (CRE) strategy may need to be adapted or ﬂexed as you go through” (E8). The fourth grouping, how to achieve the desired portfolio and improve what was being retained, included space utilisation (n = 8), CRE tactics (n = 8), workplace strategy (n = 7), restructure JERER (n = 7) and implementation (n = 6). Restructure seeks to achieve cost reduction and/or secure 14,1 the long-term occupancy of a unit. CRE tactics and implementation are closely linked in the steps needed to obtain the new portfolio. Finally, issues and consequences arising from the realignment process include landlord (n = 5) (their response to different decisions) and property management (n = 7) (certain decisions create surplus property which has consequences for property management). Surrenders and lease events reduce property management needed, but subletting increases it because a sub-tenancy creates a new layer. The HR attributes (HR proﬁle, n = 6; HR practices, n = 4 and motivation, n =2) focus on people and how they work. Considerations include properties that were no longer suitable (tagged to become surplus) and how the realignment process could create better space for people and attract talent. Both Proﬁle and Practices refer to outcomes that directly inﬂuence the amount, location and type of CRE required and, therefore, choices for retention or disposal. Wellness is regarded as an important but immature subject, which currently cannot be assessed objectively because a broad statement of beneﬁts is inadequate in the CRE decision-making process (E1). The most important group of attributes based on the overall mean is FIN (X = 7.67), followed by MAN (X = 6.29), CRE (X = 6.62) and HR (X = 4.00). The primacy of FIN attributes seems to reﬂect the ongoing CRE cost focus by management (Stadlhofer, 2010). 4.1.4 Beneﬁt variables. The end values of the causal chains in the network are the beneﬁts and represent the impacts of realignment decisions on the individual’s activated needs in the decision problem. The beneﬁts elicited are categorised as CRE, ﬁnancial and HR beneﬁts. Primary CRE beneﬁts include timing and ease of achieving the outcome (implementation beneﬁts, n = 8), opportunity and disposability proﬁles (CRE proﬁle, n =6) and the adaptability and ﬂexibility to future proof the CRE (CRE future prooﬁng, n =4). Financial beneﬁts emphasise reducing costs (cost reduction, n = 5), change to business metrics (n = 4) together with the impact on proﬁt and loss and the balance sheet (n = 3). All affect shareholder value and investor perception, as “the beneﬁt of increased proﬁts is shareholder wealth, that is in everybody’s interest” (E2). HR beneﬁts consider what the business currently has and the effect of change on its people and their motivation. This includes direct beneﬁts from successfully realigning the portfolio for the CRE team and the C-suite (HR beneﬁts, n = 5 and motivational drivers, n =5). For the CREM important motivators are self-esteem and the view of peers, whilst the beneﬁt related to the C-suite is attuned to the success of the new business strategy, of which CRE realignment is part. The impact on the people through attracting talent was not seen to be as important for the CREM (HR talent, n =3). Next are MAN beneﬁts. Financial stakeholders (n = 8) encompasses shareholders, potential investors, banks and ﬁnancial institutions. Risk reduction (n = 7) includes both containment and management of risk. Operational beneﬁts (n = 6) incorporates the needs of and the consequences for the operation of the business. Stewardship (n = 5) includes corporate governance, accountability and resolving conﬂicts, namely, how the organisation remains compliant with various regulations. Overall beneﬁts have less dominance of individual variables, only three of the 19 beneﬁts having n 7. The beneﬁt groups, based on the overall mean, have three with very close scores; MAN (X = 4.67), followed by CRE (X = 4.17) and HR (X = 4.33), while the least important is FIN (X = 3.20). So, to conclude for Question 1a, moving through the network from attributes to beneﬁts we see the change from CRE being the most probable attribute to ﬁnancial, management and HR beneﬁts being more likely. The beneﬁts identiﬁed illustrate the range of factors and the complexity of their inter-relationships that come into play even for a relatively narrow decision-making scenario. The corollary of this is that it clearly identiﬁes the diversity of the Corporate real skill-set a CREM is required to have beyond the immediate CRE technical skills. estate decision- For Question 1 b, the variable means are decisions X = 8.25, attributes X = 6.48, making beneﬁts X = 4.32 and situational X = 5.29. The higher means indicate greater agreement between the experts in their potential decision variables. However, diversity of variables increased through the MR moving from attributes to beneﬁts reﬂecting different MRs. This pattern is replicated within the CRE variables (decisions X = 8.25, attributes X = 6.62, beneﬁts X = 4.17 and situational X = 7.00). Such a pattern implies that there is not much agreement about what the end values are of CRE realignment. The FIN variables attributes have a high mean (X = 7.76) but are low as a beneﬁt(X= 3.20), again reﬂecting divergence on the end values. The mean for MAN beneﬁt (X = 4.67) is also less than for attributes (X = 6.29). Finally HR variables shows a small increase in importance between attribute (X = 4.00) and beneﬁt (X = 4.33), so it is more of an “end value”. 4.2 Question 2: ﬂow between decision-attribute-beneﬁt variables Now that the individual variables (the key considerations of the expert in their decision-making) are clear, the links between the variables, decision-attribute (D-A) and attribute-beneﬁt(A-B), will show the ﬂow of the decision-making thought process. The full decision-attribute-beneﬁt(D-A-B) chain shows which attributes are considered for individual decisions and for what reasons (beneﬁts). Table 2 presents for each attribute the links with decisions (D-A) and the links with beneﬁts (A-B) to provide insights in the chains (D-A-B). The totals on the left hand side represent the average number of incoming links (D-A links) for each attribute which is the sum of occurrence probabilities of D-A links across decision variables, D. On the right hand side it is the average number of outgoing links (A-B links), which is the sum of occurrence probabilities of A-B linksacrossbeneﬁts, B, in MRs. The numbers in the cells for the individual decisions and beneﬁts represent the occurrence probability or, in other words, the proportion of decision-makers that have considered it. For example lease event–risk = 1.0 and risk–ﬁnancial stakeholders = 0.78. The probabilities can be interpreted as a measure of the strength of the link across the decision- makers with a maximum of W = 1.0, that is all the experts mentioned it. The totals (average incoming links for D-A and the average outgoing links for A-B) can be interpreted as a measure of centrality of the attribute regarding the decisions and beneﬁts, respectively. In the following we will use the symbol “W” for this centrality measure (e.g. decision-risk = 3.33 and risk- beneﬁts = 3.44). The attributes, in the centre of the table, are ordered from high to low of potential beneﬁts associated with the attribute. The attributes discussed are those that have a centrality of 2 or more (W 2), for either D-A and/or A-B, and these are identiﬁed by bold numbers in the table. So, we examine attributes based on their centrality; the more decisions and/or beneﬁts an attribute connects to, the more important it is. There are four attributes that achieve the centrality threshold of W 2 for both D-A and B-A links (risk, IFRS16, cost and business metrics) plus two further attributes for D-A links only (ﬁnancial and external shareholders) and one for A-B link only (lease detail). With 4 decisions, 29 attributes and 17 beneﬁts visualisation of all of the links would create a very complex network. A network has been produced (Figure 3) for a selected group of variables to illustrate the complexity of the networks. risk (W [D-A] =3.33; W [A-B] = 3.44), includes risk mitigation and providing certainty. It was identiﬁed as being important because “you always look at your least risk scenario” (E3). The risk proﬁle differs between decision options. Certain decisions such as a surrender (landlord deal w = 1.0) or a lease expiry (lease event w = 1.0) provide certainty and were JERER 14,1 Table 2. Occurrence frequencies of decision-attribute links and attribute- benefit links across nine interviewees Decisions Benefits Space Landlord Lease Average Average Profit Utilisation Deal Third Party Deal Event Incoming Outgoing Financial Motivation Business and Mean Mean Mean Mean Links Ranking Attributes Ranking Links Stakeholders Drivers Metrics Loss 0.44 1.00 0.89 1.00 3.33 1 Risk 1 3.44 0.78 0.67 0.44 0.33 0.22 0.44 0.33 0.89 1.89 7 Lease Detail 2 3.33 0.78 0.56 0.33 0.22 0.22 0.89 0.89 1.00 3.00 4 Cost 3 3.22 0.78 0.56 0.33 0.22 0.33 1.00 1.00 1.00 3.33 1 IFRS16 3 3.22 0.89 0.56 0.44 0.33 0.11 0.89 0.78 1.00 2.78 5 Business Metrics 5 2.22 0.67 0.33 0.33 0.11 0.04 0.04 0.44 0.11 0.63 16 Prop Mgt 6 1.78 0.22 0.33 0.11 0.22 0.00 0.33 0.44 0.56 1.33 9 Cash 7 1.67 0.56 0.33 0.22 0.11 0.22 0.89 1.00 1.00 3.11 3 Financial Analysis 8 1.56 0.33 0.33 0.00 0.00 0.11 0.33 0.22 0.56 1.22 10 CRE Tactics 8 1.56 0.22 0.11 0.11 0.22 0.00 0.11 0.22 0.44 0.78 15 Proposed Portfolio 8 1.56 0.33 0.22 0.22 0.00 0.00 0.67 0.78 0.67 2.11 6 Stakeholders - External 11 1.33 0.56 0.11 0.11 0.11 0.00 0.33 0.44 0.33 1.11 12 Decision-making 11 1.33 0.33 0.33 0.00 0.00 0.11 0.11 0.22 0.44 0.89 14 CRE Metrics 13 1.22 0.22 0.44 0.00 0.00 0.11 0.44 0.56 0.56 1.67 8 CAPEX 14 1.11 0.33 0.11 0.11 0.11 0.00 0.11 0.00 0.22 0.33 23 HR Proﬁle 14 1.11 0.33 0.11 0.11 0.11 0.00 0.00 0.00 0.11 0.11 28 Workplace Strategy 14 1.11 0.22 0.11 0.11 0.22 0.00 0.22 0.11 0.22 0.56 18 Space utilisation 17 1.00 0.22 0.11 0.11 0.11 0.11 0.33 0.33 0.22 1.00 13 Corporate Governance 18 0.78 0.33 0.11 0.00 0.00 0.11 0.11 0.11 0.11 0.44 20 CRE Designation 19 0.67 0.00 0.11 0.00 0.00 0.11 0.00 0.11 0.22 0.44 20 Operational Issues 20 0.56 0.11 0.00 0.11 0.11 0.00 0.11 0.11 0.33 0.56 18 Current Portfolio 21 0.44 0.11 0.11 0.00 0.00 0.00 0.22 0.33 0.67 1.22 10 Implementation 21 0.44 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.11 0.22 25 Restructure 21 0.44 0.22 0.00 0.00 0.11 0.00 0.11 0.11 0.00 0.22 25 Business Strategy 24 0.33 0.11 0.00 0.00 0.00 0.00 0.22 0.11 0.33 0.67 16 Landlord 24 0.33 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 29 CRE Market Risk * 26 0.22 0.11 0.11 0.00 0.00 0.00 0.11 0.11 0.11 0.33 23 HR Practices 26 0.22 0.00 0.00 0.00 0.00 0.00 0.11 0.11 0.22 0.44 20 Motivation 26 0.22 0.11 0.11 0.00 0.00 0.00 0.11 0.11 0.00 0.22 25 Stakeholders - Internal 26 0.22 0.11 0.00 0.00 0.00 0.08 0.32 0.34 0.43 Average 0.31 0.20 0.11 0.09 Note: *CRE Market Risk included as the “0” was the result of rounding (continued) Corporate real estate decision- making Table 2. Decisions Benefits Space CRE Utilisation Implementation Risk Future Cost Operational Business HR CRE Proposed HR Mean Benefits Stewardship Reduction Proofing Reduction Benefits Prospects Benefits Profile Cash Portfolio CAPEX Talent 0.44 0.33 0.11 0.56 0.00 0.11 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.33 0.22 0.44 0.11 0.11 0.11 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.22 0.22 0.22 0.11 0.00 0.11 0.11 0.22 0.00 0.11 0.11 0.00 0.00 0.11 0.33 0.00 0.22 0.11 0.00 0.11 0.11 0.22 0.00 0.00 0.11 0.00 0.11 0.00 0.11 0.11 0.00 0.00 0.11 0.11 0.22 0.00 0.11 0.11 0.00 0.00 0.00 0.00 0.04 0.00 0.11 0.22 0.22 0.11 0.00 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.11 0.11 0.00 0.00 0.11 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.11 0.00 0.00 0.22 0.11 0.22 0.00 0.00 0.11 0.11 0.00 0.00 0.00 0.11 0.22 0.11 0.11 0.22 0.11 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.22 0.00 0.11 0.00 0.00 0.11 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.11 0.00 0.11 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.11 0.00 0.11 0.00 0.22 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.11 0.00 0.00 0.11 0.00 0.11 0.00 0.11 0.00 0.00 0.11 0.00 0.00 0.11 0.11 0.11 0.00 0.00 0.11 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.11 0.00 0.00 0.00 0.00 0.11 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.11 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.11 0.00 0.11 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.22 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.22 0.22 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.09 0.08 0.07 0.06 0.05 0.05 0.04 0.03 0.03 0.02 0.01 0.01 0.01 JERER 14,1 Figure 3. Decision network example identiﬁed by all experts. In contrast assignments and subletting (third party deals) introduce risk from a third party and create “a black box in terms of something going wrong” (E2). Regear and new lease both remove short-term risk, but long-term CRE requirements may change rendering the decision erroneous. Consequently a CREM seeks to understand future needs to avoid such issues (E5). Freehold purchase increases short-term risk but on disposal of the freehold, the risk will be removed. However, as there is rarely certainty at commencement of the process it is disliked by the C-suite, they seek certainty so that targets communicated to the ﬁnancial markets (ﬁnancial stakeholders w = 0.78) are achieved. Therefore, as a beneﬁt it is deemed important. Success or failure in achieving targets reﬂects on individuals thence performance and bonus payments. Unexpected events/costs have a direct inﬂuence on achieving targets (motivation drivers w = 0.67). Risk is not included in alignment models (Lindholm et al.,2006) and this research has shown that its omission is erroneous. Models need to include what is a signiﬁcant CREM and business concern. The over-riding issue for several experts was not the absolute value of a variable, but the risk of variance from the budgeted or reported ﬁgure. Next is IFRS16 (W [D-A] = 3.33; W [A-B] = 3.22). Its importance may reﬂect its Corporate real introduction a few months before the interviews. A reduction (e.g. a lease expiry; lease event estate decision- w = 1.0) or an increase (e.g. a regear; landlord deal w = 1.0) will impact company making performance and consequently beneﬁts of ﬁnancial stakeholders (w = 0.89) and motivational drivers (w = 0.56). Certainty and avoiding overpayment are key for the attribute of Cost (W [D-A] = 3.30; W [A-B] = 3.22). The true cost of a third party deal will not be known until lease expiry, because of the risk of default of the third party. Experts were concerned how costs from a transaction impacted shareholders and potential investors and how variance from projected costs might affect perceptions. With a lease event (w = 1.0) there should be certainty of cost and hence its importance. These lead to the strong links with ﬁnancial stakeholders (w = 0.78) and motivation drivers (w = 0.56). Two attributes that link CRE with business performance are business metrics (W [D-A] = 2.78; W [A-B] =2.22) and ﬁnancial analysis (W [D-A] =3.11; W [A-B] =1.56). Financial analysis appears to be more important for decisions, possibly reﬂecting its internal focus rather than the broader based Business Metrics, but it has a weak link with ﬁnancial stakeholders beneﬁts (w = 0.33) compared to business metrics beneﬁts (w = 0.67). For both attributes the link to motivation drivers beneﬁts (w = 0.33) is relatively weak. External stakeholders (W [D-A] =2.11; W [A-B] =1.33) has strong links with three of the four decisions, lease events, landlord deals (both w = 0.67) and third party deals (w = 0.78) because of the importance of landlords and potential tenants in the individual decision implementation. For the A-B links only ﬁnancial stakeholders (w = 0.56) showed a degree of centrality with the experts. Finally lease detail (W [D-A] =1.89; W [A-B] = 3.33) is critical for lease events (w = 0.89). Lease break clauses in England and Wales are complex requiring tenant compliance with conditions creating a risk a break cannot be successfully operated. Both events provide low cost solutions and certainty producing direct beneﬁts to ﬁnancial stakeholders (w = 0.78) and generating self-esteem and peer recognition from the professional expertise required (motivation drivers w = 0.56). In conclusion, for the scenario of reducing cost and space, the links between decision, attribute and beneﬁt variables are typiﬁed by CRE decisions that consider risk and ﬁnancial attributes that have beneﬁts to ﬁnancial stakeholders and create motivational drivers for both the C-suite and CREM’s. 5. Conclusion and implications for theory This research has sought to “lift the lid” on the CRE decision-making process and unravel its complexity. It does so by using CNET to investigate the MR’s of decision-makers when they are presented with the problem of portfolio realignment. Speciﬁcally it considered a scenario of reducing property costs and units, asking what situational variables inﬂuence the MR’s, which decisions are identiﬁed to solve the problem, what attributes are associated with each decision and for what beneﬁt(s) those attributes are considered. It has sought to gauge the relative importance of each variable. The important situational variables were business strategy, data and the property market. The signiﬁcance of the CRE and business strategy link has been identiﬁed in various papers (Gibler and Lindholm, 2012). The acquisition process has been discussed (Nunnington and Haynes, 2011) and both business data and the property market received attention in that context. The decision variables identiﬁed here are well established options for practitioners, conﬁrmed by the commonality of identiﬁcation. The low space utilisation scores may reﬂect the speciﬁed scenario or different sectoral issues, ofﬁce space being more ﬂexible for reconﬁguration than others. In addition, CRE managers have different JERER experiences which inﬂuence their decisions. 14,1 Attribute variables were wide ranging, but ﬁnancial ones were prominent reﬂecting the on-going focus on cost (Stadlhofer, 2010). A trade-off exists between the future beneﬁts of dynamic alignment and cost implications now (higher rents and reduced incentives). The methodology did not ask the experts to choose between the two. The occurrence of IFRS16, business metrics and ﬁnancial analysis suggests that models need a broader ﬁnancial perspective, to include those considered pertinent by practitioners. Beneﬁts are primarily ﬁnancial stakeholders and motivational drivers. Alignment models have the overarching objective of “maximising shareholder wealth” (Gibler and Lindholm, 2012). This research shows that shareholders are important, but a wider range of inﬂuences operate, including share price and dividends and that shareholders are not the sole priority for decision-makers (as per Stout, 2012). Motivational drivers identiﬁed in this research indicate the importance of self-esteem and peer recognition for CREM’s and ﬁnancial beneﬁts for the C-suite, different objectives between individuals and the ﬁrm have been discussed elsewhere (Mintzberg et al.,2009). The CREM has been identiﬁed as an enabler and tactical decision-maker not the CRE strategy decision-maker. As the experts were responding based on their total experience over a number of organisations this does not simply reﬂect the position in nine ﬁrms. Discussions on added-value from CREM activities (Jensen et al.,2012) need to be reconsidered in light of this and the matter warrants further research. Additionally emphasis on HR issues and corporate social responsibility (Gibler and Lindholm, 2012)is not replicated in this research, suggesting that decision-makers do not see the beneﬁts from them (yet). What is not clear is whether CRE is recognised as an intangible resource by ﬁrms (Heywood and Kenley, 2008). Alignment models need to incorporate the ﬁndings in this research, especially the important attribute and beneﬁt variables plus they should reﬂect that, “alignment is more complex and pluralistic than individual models portray” (Heywood and Arkesteijn, 2018, p. 18). There is a gap between theoretical modelling and the real world, more research is required to close that gap by exploring what happens in the real world. Gaining a greater understanding of the rationale for decisions could provide solid grounding for future theories. The behavioural research approach adopted has been demonstrated to provide a methodology for CRE decision- making analysis. The CNET technique provides the interviewer with considerable information and offers opportunities to explore CRE issues in the way questionnaires do not. 6. Limitations and recommendations for practice The present research has focussed on the centrality (homogeneity) of responses to variables across decision-makers. What should not be ignored is the heterogeneity that arises from the 722 variables identiﬁed. The diversity of responses and the interconnectivity of the networks produced from the interviews highlights the complexity of the decision-making process, how individual CREM’sinﬂuence decision-making in the C-suite and its implementation. The comparison of quoted and privately owned companies plus the public sector could provide interesting insights and different variables. A focus on different sectors (e.g. ofﬁces and retail) with a different scenario (e.g. acquisition) could highlight commonality and differences between the two. Time constraints meant that only one interview took place with each participant. A follow up interview(s) would have greatly assisted in generating more information and detail on the MR’s. Experts were all CREM’s and extending the process to the C-suite would allow the exploration of the role of those individuals in decision-making (Greenhalgh, 2008). In parallel comparing decision-making between owners and managers could assist in assessing the issues around shareholder focus Corporate real and whether that ﬂows through to CRE decisions. The iterative decision-making process estate decision- (Chiva et al., 2014) is not examined here either, nor the inter-relationship between CREM and making senior management, bringing senior management into the research would facilitate this. The paper provides clear evidence of the complexity of CRE decision-making for the C-suite, that complexity needs to be integrated into the systems within the ﬁrm and the strategy development process. The capacity for change within a business is increasingly important in these turbulent and uncertain times. Decisions by the C-suite on CRE strategy need to recognise the challenges that CRE speciﬁcally poses. Another practical consideration of the research is the conﬁrmation of the multi-layered complex decision-making process for CRE. 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Howard Cooke is the corresponding author and can be contacted at: firstname.lastname@example.org Rianne Appel-Meulenbroek is an Associate Professor in CRE and workplace at the Department of the Built Environment at Eindhoven University of Technology. Her research approaches CRE as an important strategic resource for (knowledge) organisations and focusses on how it should be managed strategically and how location, building and workspace design choices for modern work environment concepts add value to driving forces of the users. Theo Arentz is a Full Professor of real estate and urban systems and a Chair of the Real Estate Management and Development group in the department of the Built Environment of the Eindhoven University of Technology. His research focuses on the analysis and modelling of spatial choice behaviour of users in the built environment and application of these models for large-scale simulation and decision support. 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Journal of European Real Estate Research – Emerald Publishing
Published: Jun 4, 2021
Keywords: Decision-making; Alignment; Corporate real estate; Decision networks; Mental representations
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