The aims of this chapter are to:

• introduce the challenges of writing for organizational and academic audiences;

• focus the production of accounts of change by examining critical questions that help frame the account; and

• prepare those studying change in the context of assessed courses to produce academically grounded accounts of change.

The role of accounts in organizational life has been studied extensively, and such accounts are obviously related to the narrative view of organizations discussed in Chapter 13. Accounts are defined as a linguistic device employed whenever an action is subject to a process of enquiry (Scott and Lymann, 1968). Scholars have considered the contents of such accounts, the conditions under which people present accounts and – perhaps most interestingly for our purposes – the conditions under which people accept the accounts of others (see Orburch, 1997). Erving Goffman argues that narrative accounts are created for existing audiences in order to help maintain social identities (1959; 1971). In this chapter we consider both the rationale for providing an account and the audiences to which these accounts are presented. In terms of rationale or impetus, we would highlight two related but distinct reasons for giving an account of change. First, organizational members must often give an account of the change process in which they are involved. This could be to signify that the change has drawn to a conclusion or it could be to assess progress in an ongoing process. In either case, there is a need to report an account of the change to a particular set of audiences. A second form of account is often required when studying change in the context of an academic programme as part of an assessed qualification, such as a certificate, diploma or degree. Both types of account are subject to some scrutiny and assessed. One type of account may be used to inform the other, but both merit consideration in their own right.

Today, Apple is one of the most successful firms in the world, with a string of hit products and a loyal group of customers. Indeed, in August 2011 Apple was cited as the most valuable company in the world when measured by market capitalization (Rushe, 2011). Apple’s current success follows periods of real turbulence, and co-founder Steve Jobs was ousted from the firm in 1985 during an earlier period of poor performance. In 1997 Michael Dell (of Dell Computers) was asked how he would deal with the significant problems that Apple was facing at that time, and he replied: ‘I’d shut it down and give the money back to shareholders.’ With the benefit of hindsight, Apple’s shareholders will be glad that Dell’s advice was not taken.

Steve Jobs and Steve Wozniak founded Apple Computer in 1976 and launched their first product, the Apple I, from the Jobs family garage. The launch of their second product, the Apple II in 1977, was a major success, and helped shape the emerging market for personal computers. Apple quickly became a market leader, selling more than 100,000 Apple IIs by the end of 1980. When IBM entered the PC market in 1981 and a new software firm, Microsoft, began selling the DOS operating system, Apple faced a new competitive landscape. The now familiar tale was that IBM created a template that other manufacturers could clone with third-party software able to run on any compatible computer. In contrast, Apple cherished its proprietary hardware and was reluctant to enter into licence agreements. IBM’s approach helped the company establish a new industry standard and drove up market share. Apple’s products were seen as easier to use, and the firm was the first to introduce the use of a mouse and a graphical user interface, which we now see as the default way of interacting with most computers. Although Apple was seen as the more elegant product offering, the lack of compatible software inhibited sales, and the firm’s net income deteriorated significantly during the early 1980s. Eventually the board forced Jobs to leave.

This aims of this chapter are to:

• show how processes relate to other aspects of organizations, such as people and structures;

• locate process-oriented change research within the literature; and

• introduce basic techniques for process mapping and suggest ways in which process changes might draw on such modelling approaches.

As discussed in Chapter 7, it is apparent that as organizations grow they begin to formalize the way in which business is conducted, because all organizations reach a size at which an informal approach is no longer tenable. Everything from recruiting staff and choosing suppliers to designing products and managing quality can be described in terms of the sequence of activities involved. The larger, more established and more complex the organization is, the more formal the documentation of such activities can become, and many large organizations have lengthy documentation setting out the way in which tasks are accomplished. Whilst this is sometimes necessary for accreditation purposes, such as ISO 9001, such descriptions can be cumbersome. Partly because such documentation is long and detailed and has a number of interconnects with other aspects of organizational life, it can be very challenging to introduce change. Change to approved, accredited or simply familiar ways of doing things might need to involve multiple stakeholders (see Chapter 5) and can be time-consuming.

Admiral Group plc was launched in 1993 by a start-up team of five people with prior experience in financial services. Most of the group had completed MBAs, and they saw a gap in the market for a new insurer. From an insurer’s point of view, those who presented the lowest risks were viewed as the most attractive customers. Those with a long and unblemished history of driving, living in the suburbs and – preferably – driving a modest but safe car were seen as the best kinds of customers, because they were statistically less likely to make a claim. Admiral was keen to offer insurance to those who were not traditionally seen as safe drivers. This meant that they targeted younger drivers, owners of faster and more powerful cars, those living in inner cities or people with combinations of these characteristics. The logic was simple. The most important thing was finding an appropriate premium for an appropriate level of risk. By targeting customers that other insurers often ignored, Admiral was able to exploit a space in what was a mature and problematic industry in which many of the incumbent firms made losses on a regular basis.

Based in the Welsh capital, Cardiff, Admiral is now a highly profitable group of companies operating in the United Kingdom and in several international markets. Henry Engelhardt, the CEO and one of the original founders of the business, believes that senior managers should be accessible to staff. In the company’s own words, Admiral feels: ‘People who like what they do, do it better.’ Hence, the firm places a heavy emphasis on staff engagement. Regular staff surveys allow senior managers to get a sense of whether people enjoy working for the business, and they are very proud of the fact that Admiral regularly appears in the ‘Best places to work’ lists generated by both The Sunday Times and the Financial Times. The Ministry of Fun which Admiral runs is one way that the firm encourages staff to celebrate success by organizing a range of events and activities to build a sense of belonging within the business.

The aims of this chapter are to:

• show how the nature of learning includes different preferred styles;

• develop a model of learning for technique and insight;

• explain alternative methods of learning and development;

• illustrate the application of these methods in a mini-case study of the Boston Consulting Group; and

• establish the application of learning and development practices as part of change management.

Learning and development are central to change management. Most change projects entail people doing things differently, and major changes often mean that people need to understand the nature of the organization’s processes, relationships with customers and clients and practices of delivery differently. Such changes mean that the change manager has to understand how people develop and what can be done to enable people to enact and understand the innovations that are aspired to (Antonacopoulou, 2006).

This chapter sits between the macro issues for learning that were raised in Chapter 6 and the more person-centred issues that are discussed in Chapter 14. In Chapter 6 the distinction was made between single-loop learning (incremental improvement) and double-loop learning (radical change). In Chapter 14 we explore the nature of reflection and reflexivity and the ways that learners can challenge themselves and come to a new understanding of who they are and what they do. In this chapter we focus on the distinction between technique learning – the focus on specific, defined skills outcomes – and insight learning – the focus on developing new ways of conceiving reformatory personal change. We discuss how the alternative methods of learning and development can be integrated and the change manager’s role in selecting which methods to use to meet the demands of their situation (Easterby-Smith and Lyles, 2003).

In the late 1980s Percy Barnevik was CEO of Swedish firm ASEA, an engineering firm employing some 71,000 staff globally. He had overseen a dramatic improvement in ASEA’s performance, quadrupling sales and increasing profits by a factor of ten. By 1988 he was looking for ways to continue the growth of the firm, and he led ASEA into a merger with a larger Swiss firm, Brown Boveri, which employed around 97,000 staff. At the age of forty-six Barnevik became the first CEO of the newly merged firm ASEA Brown Boveri, or simply ABB, with a combined staff of some 160,000 people operating in 140 countries and combined sales of $15 billion. From the moment it was formed, ABB became a major player in the global engineering and technology sector.

Barnevik had big plans for the new firm, and within months of taking office as ABB’s CEO he held a gathering of a few hundred senior ABB managers to set out his vision for the future. He introduced what he called the ‘corporate policy bible’, which included details of the firm’s approach to change. He described the firm’s strategy at that point as being ‘a two-stage rocket: restructuring then growth’ (Barham and Heimer, 1998). Over the next few years he delivered both restructuring and spectacular growth. Although he was not a native English speaker, Barnevik insisted that English had to be ABB’s corporate language, and he created a new corporate identity for ABB to deliver his strategy. He focused hard on making a huge corporate organization feel like a much smaller one, citing his early experiences in his father’s small business, which had employed only fifteen people. His mantra was to create small, customer-facing business units in which the entrepreneurial flair of staff could be released. Decentralizing and individual accountability through distinct and separate profit centres were the order of the day. What followed was a period of tremendous change in the organization.

The aims of this chapter are to:

• show how conversations and interaction fit into broader social and interactive context of organizational change;

• introduce narrative analysis and its relevance for managing change; and

• discuss transactional analysis as a way of understanding and planning interaction.

It is very common for problems and successes in change management to be traced back to communications. Many reports of failure claim that there had been insufficient or ineffective communication, with the result that people did not know what they were meant to do or felt excluded. However, communicating clearly is only part of the answer. When we are communicating about complex matters such as change, people often do a lot of sense making, in which they can understand quite different things from the same set of words (Brown, Stacey and Nandhakumar, 2008). For example, a leader might explain clearly that the changes will lead to greater customer satisfaction, but different members of the audience might interpret this as meaning, for example, that there will be less autonomy for employees because they will have to follow customer demands, that the new approach will lead to better profits or that the statement is merely rhetoric and the leader does not really care about customers (amongst many other possible interpretations) (Sims, Huxham and Beech, 2009). The interpretive process can impose quite different understandings, and hence prompt very different reactions. Therefore, it is important to understand how different interpretations are made and what might be done about this when leading change (Grant and Marshak, 2011).

Our argument in this book is that much of the time in organizations we are managing change through the deliberate selection of practices that we hope will produce particular results. The triggers for such change work may emanate from within the organization or from shifts in the external environment. They may be optional or unavoidable, and they may be rapid and radical or slow and evolutionary. There are many tools and techniques that pertain to change situations, but choosing what to do, and how to do it, is not straightforward. In this book we elaborate a framework that does not dictate a prescribed path to managing change but treats the process as one of enquiry and action. This entails being skilled at asking searching questions so that the circumstances and purpose can be understood and matched to action. Action in this field is normally somewhat experimental, as even the most popular ‘tried and tested’ practices can fail in new situations. Therefore, the approach adopted here is to build up a repertoire of options and to be active both in the selection of which action option (or combination of options) to take and in the adaptation and development of change practices. Hence, change management is regarded as being based on skills of judging situations, selecting and adapting from prior practices in order to develop new ones and subsequently being able to understand and evaluate how these actions are working and thus make appropriate adjustments. In short, the change manager is an active learner, engaged in a continuous cycle of enquiry and action.

This aims of this chapter are to:

• explain the relationship between individual vitality and organizational engagement;

• explore the ways in which our experience change in organizations can shape our sense of engagement; and

• investigate the concept of health as an organizational process.

When we walk into an organization for the first time we quickly pick up something of the ambience and atmosphere of that organization. Much like viewing a home or interviewing a candidate for a job, we form first impressions quickly. In some cases it may be the creative sparks of an energetic and innovative workplace that we discern. In others it may the dour and somewhat lifeless sense that the organization is failing in some way. Since organizations are populated by people, it is natural to question the relationships between individual and collective moods. Does, for example, working in a great organization make you happy, or can a bad workplace lower your mood? Robert Quinn feels that there is a need for a ‘new and larger concept called organizational vitality’ (1978: 395). For Quinn, this concept would incorporate elements such as motivational climate, and other aspects that we might relate to organizational culture (see Chapter 6). In our own research we continue to be struck by the markedly differing accounts of organizational life that we have heard over many years of interviewing members of a variety of public, private and third sector organizations. Indeed, it has been argued that health is inherently an organizational and relational phenomenon (MacIntosh, MacLean and Burns, 2007). In the early part of the last century Alfred North Whitehead observed that ‘prolonged routine work dulls the imagination’ (1929: 144) and that ‘it is a libel upon human nature to conceive that zest for life is the product of pedestrian purposes directed toward the narrow routine of material comforts’ (140). Vitality might therefore have its roots in the nature of our organizational experience, since health and disease have both been conceptualized as organizational phenomena (MacLean and MacIntosh, 1998).

The goal of computer vision is to extract useful information from images. This has proved a surprisingly challenging task; it has occupied thousands of intelligent and creative minds over the last four decades, and despite this we are still far from being able to build a general-purpose “seeing machine.”

Part of the problem is the complexity of visual data. Consider the image in Figure 1.1. There are hundreds of objects in the scene. Almost none of these are presented in a “typical” pose. Almost all of them are partially occluded. For a computer vision algorithm, it is not even easy to establish where one object ends and another begins. For example, there is almost no change in the image intensity at the boundary between the sky and the white building in the background. However, there is a pronounced change in intensity on the back window of the SUV in the foreground, although there is no object boundary or change in material here.

We might have grown despondent about our chances of developing useful computer vision algorithms if it were not for one thing: we have concrete proof that vision is possible because our own visual systems make light work of complex images such as Figure 1.1. If I ask you to count the trees in this image or to draw a sketch of the street layout, you can do this easily.

In the last chapter we showed that classification with generative models is based on building simple probability models. In particular, we build class-conditional density functions Pr(x|w = k) over the observed data x for each value of the world state w.

In Chapter 3 we introduced several probability distributions that could be used for this purpose, but these were quite limited in scope. For example, it is not realistic to assume that all of the complexities of visual data are well described by the normal distribution. In this chapter, we show how to construct complex probability density functions from elementary ones using the idea of a hidden variable.

As a representative problem we consider face detection; we observe a 60 × 60 RGB image patch, and we would like to decide whether it contains a face or not. To this end, we concatenate the RGB values to form the 10800 × 1 vector x. Our goal is to take the vector x and return a label w ϵ {0,1} indicating whether it contains background (w =0) or a face (w = 1). In a real face detection system, we would repeat this procedure for every possible subwindow of an image (Figure 7.1).

We will start with a basic generative approach in which we describe the likelihood of the data in the presence/absence of a face with a normal distribution. We will then extend this model to address its weaknesses.

This chapter provides a brief overview of modern preprocessing methods for computer vision. In Section 13.1 we introduce methods in which we replace each pixel in the image with a new value. Section 13.2 considers the problem of finding and characterizing edges, corners and interest points in images. In Section 13.3 we discuss visual descriptors; these are low-dimensional vectors that attempt to characterize the interesting aspects of an image region in a compact way. Finally, in Section 13.4 we discuss methods for dimensionality reduction.

Per-pixel transformations

We start our discussion of preprocessing with per-pixel operations: these methods return a single value corresponding to each pixel of the input image. We denote the original 2D array of pixel data as P, where pij is the element at the ith of I rows and the jth of J columns. The element pij is a scalar representing the grayscale intensity. Per-pixel operations return a new 2D array X of the same size as P containing elements xij.

Whitening

The goal of whitening (Figure 13.1) is to provide invariance to fluctuations in the mean intensity level and contrast of the image. Such variation may arise because of a change in ambient lighting intensity, the object reflectance, or the camera gain. To compensate for these factors, the image is transformed so that the resulting pixel values have zero mean and unit variance.

In Chapter 11, we discussed models that were structured as chains or trees. In this chapter, we consider models that associate a label with each pixel of an image. Since the unknown quantities are defined on the pixel lattice, models defined on a grid structure are appropriate. In particular, we will consider graphical models in which each label has a direct probabilistic connection to each of its four neighbors. Critically, this means that there are loops in the underlying graphical model and so the dynamic programming and belief propagation approaches of the previous chapter are no longer applicable.

These grid models are predicated on the idea that the pixel provides only very ambiguous information about the associated label. However, certain spatial configurations of labels are known to be more common than others, and we aim to exploit this knowledge to resolve the ambiguity. In this chapter, we describe the relative preference for different configurations of labels with a pairwise Markov random field or MRF. As we shall see, maximum a posteriori inference for pairwise MRFs is tractable in some circumstances using a family of approaches known collectively as graph cuts.

To motivate the grid models, we introduce a representative application. In image denoising we observe a corrupted image in which the intensities at a certain proportion of pixels have been randomly changed to another value according to a uniform distribution (Figure 12.1).

In this chapter, we consider a pinhole camera viewing a plane in the world. In these circumstances, the camera equations simplify to reflect the fact that there is a one-to-one mapping between points on this plane and points in the image.

Mappings between the plane and the image can be described using a family of 2D geometric transformations. In this chapter, we characterize these transformations and show how to estimate their parameters from data. We revisit the three geometric problems from Chapter 14 for the special case of a planar scene.

To motivate the ideas of this chapter, consider an augmented reality application in which we wish to superimpose 3D content onto a planar marker (Figure 15.1). To do this, we must establish the rotation and translation of the plane relative to the camera. We will do this in two stages. First, we will estimate the 2D transformation between points on the marker and points in the image. Second, we will extract the rotation and translation from the transformation parameters.

Two-dimensional transformation models

In this section, we consider a family of 2D transformations, starting with the simplest and working toward the most general. We will motivate each by considering viewing a planar scene under different viewing conditions.

Euclidean transformation model

Consider a calibrated camera viewing a fronto-parallel plane at known distance, D (i.e., a plane whose normal corresponds to the ω-axis of the camera).