How Age Affects Management Styles

.@JBirkinshaw @JNManktelow et al writing in the MIT Sloan School of Management Journal, @mitsmr of a survey of managers with a view to uncovering how Age impacts management styles via biological, generational, seniority-& experience-related effects. Such effects are often intertwined in their expression, even when a clear separation on a conceptual basis is possible. Their paper  report on the survey - of 10,000 managers, with ages in the range 21-70, and they discuss the nuances of their results. Overall, they contrast the external and internal (to the firm) focus as evident in their attitudes, actions and survey responses. They find that older managers focus more on Core Competencies of the firms and Client relationships as part of their external focus; but their focus within the firm is on building coalitions, developing empathy and  on effectively delegating functions.  While by contrast, younger managers focus more on learning about or developing entirely new Business Models & on their company's Competitive Positioning externally;  and on finding a good Mentor within the company or developing training programs as part of their internal focus.

 

The overall set of results broadly resonate with conventional wisdom, but is invaluable in emphasizing the broad complementarity in what older and younger managers can bring to the table, and that age-diversity is as critical for a firm as diversity along any other axis. Even more, the authors go out of their way to assert that the qualities of older managers *(such as being reflective, intuitive, savvy, holistic or inclusive) cannot, quite obviously, be replaced by any 'artificially intelligent' machine!

CRISIL's Report on 'Whither Inflation?'

.@CRISILLimited presents a very deep dive on 'Whither #Inflation?' Many excellent insights emerge fro their analysis, especially on India #Food price inflation. They provide a great survey-based comparison of the general public's Inflation #Expectations: Before and After the introduction of Flexible Inflation Targeting in India (it was introduced 27 June 2016) and a particularly interesting comparison of the evolution of inflation expectations among the Public versus how they evolved for Forecasters.  

 

While public inflation expectations did indeed come down after Flexible Inflation Targeting was introduced, the expectations are still noticeably higher than actual, realized inflation. And while realized inflation did come down after Flexible Inflation Targeting was introduced, that was mostly because food inflation decreased (and that happened from idiosyncratic factors such as a good monsoon). The bottom line is that while both inflation and inflation expectations came down after Flexible Inflation Targeting, the decrease cannot be unambiguously attributed to monetary policy actions. Indeed, when FIT was proposed as a monetary framework for India, the biggest drawback foreseen was in monetary policy transmission, which even now, with the FIT framework being nearly 3 years old, is still an issue. via @EconomistDK

Will 5G Networks Give Chinese Firms a First-Mover Advantage?

Of 22 panelists that the MIT Sloan School Strategy Forum (Organized by the MIT Sloan Management Review, @mitsmr)  polled, on how strongly they agree with the statement 'Introducing 5G networks 3-5 years ahead of other countries will give Chinese firms a (#FirstMover) advantage' 19 panelists said they 'Agree or Strongly Agree'; only Timothy Simcoe of the Boston University Questrom School of Business  (@BUQuestrom) said that he #StronglyDisagreed. However. he did concede that wireless equipment makers (like transmitters, routers, towers, distributed antenna systems, interference mitigation devices, resource management software and hardware, ie all kinds of wireless equipment, but not the phones themselves) like @Huawei/@ZTEPress will have an advantage (but on cost, resulting from their overall cost structure, not as first-movers); while data carriers [because they will compete locally with other carriers in China, where 5G networks will already have high penetration] or device makers [who will all simply take the available 5G network as standard, so that it will not be a distinguishing feature between them] will have no advantage whatever). (Note: Huawei and ZTE are notable mainly for the equipment they make, but may also make phones. In that case, their equipment manufacturing divisions will gain on cost, not on first-mover, while the phone manufacturing divisions will not gain at all.)

Olav Sorenson (@OSorenson)  is the only other panelist who disagrees with the motion, but does not 'Strongly Disagree' - says that the question is moot because 5G is already being deployed, eg in USA and (South) Korea. One panelist, Richard Florida, hedged completely, neither agreeing nor disagreeing.

Nineteen of the twenty-two panelists (which includes, coincidentally, my old friend Ashish Arora of Duke University) thus agreed that introduction of 5G networks will give Chinese firms a first-mover advantage. To my mind, it was Tim Simcoe's disagreement that was best argued, being based on detailed knowledge of both technical and market issues, while all the rest, in agreeing, did not present any overwhelmingly compelling arguments, although some panelists did enlist 'network externalities', 'control of standards', 'learning-by-doing', 'scale economies', or the 'it depends on the merits of the Chinese technology' arguments. These are pretty old ideas and it is not absolutely clear that they do indeed apply in unmodified form, to the problem at hand. Erik Brynjolfsson's  'argument' if one could call it that, and even, or especially, if one agrees with it - was more a political statement than anything else: '5G is a Big Deal, and the US is fumbling its rollout'. Barry Nalebuff of Yale brings up a historical vignette - the case of France's Minitel (the introduction of which, in the early 1980s) was thought to provide French internet/phone companies a huge first-mover advantage. As things turned out, the advantage was very small or non-existent.

In the end, any decisions on 5G, and Huawei cannot be expected to be made solely on techno-economic arguments of the sort that Tim Simcoe provides, geoeconomics is likely to be much more important - an issue no panelist brought up even tangentially.

Jonathan Garner of Morgan Stanley on BloombergQuint

Jonathan Garner of @MorganStanley speaks with @BloombergQuint on is expectations for the Indian economy following the release of Exit Poll results on the 2019 General Elections (but pending the results themselves). He expects the Reserve Bank of India, @RBI to lower rates by about 50bp over the next 12 months; he expects that GDP growth will rise (more in an upward mean-reverting phenomenon than in reacting to any specific positive development). He expects this to happen as the Indian economy recovers from the series of demand and/or liquidity shocks it has experienced over the last 2 years - including demonetization, the introducton of the GST, the IL&FS and the non-banking financial companies scandals, and the monetary policy actions by the RBI [rate increases during 2018]). He expects as a consequence that the Indian Rupee will appreciate against the US dollar, to about $ @ mid60s₹  over the next 12-18 months; and the Sensex to rise to 42,000 some what sooner, over the next 12 months.

The AI Bubble

Gerbert & Shira of the Boston Consulting Group, @BCG write in the MIT Sloan Management Review @mitsmr that 'yes, today’s fascination with all things #AI has most of the trappings of a financial #bubble...in most cases there is no clear path for (AI startup) companies to become profitable.' Under the circumstances, small start-ups, having developed their technology, or having run out of venture capital money, will seek to be acquired by larger companies. These acquiring companies, they warn, should be wary. Two things may be worth noting when assessing AI startup companies - first, most algorithms (machine learning especially) used in AI applications are several decades, if not half a century old (a version of backpropagation was, for example, used in the Apollo-11 moon landings). The main new development has been in the development of faster computers on which machine learning algorithms can run, and in the availability of large new data sets on which neural nets/machine learning algorithms can be trained. 

But the first development is ironic in a way - in that, neural nets were intended to be archetypes of parallel computer architectures, so the fact that they are simulated in conventional (though very fast) computers of the conventional type should give one some pause. To be sure, there are some new special-purpose hardware architectures (including new chips) which are designed to optimize the computational power used by neural networks. But this has not yet become altogether commonplace. But even when this comes to become more common, rare will be the startup that owns the data which it will use to train its neural network application. The lack of clear ownership of data means that a startup may not be able to optimize and customize its application to the ultimate user, while the machine learning algorithms themselves are publicly available practically, or actually, for free. Thus, on the critical axis of value creation: the trained network, a crucial aspect - the data used - is not owned by the startup. How then can the typical AI startup aim for profitability? What will be its critical determinant of value and distinguishing characteristic? These questions are precisely the ones that acquiring companies will need to ask themselves and the startups they hope to acquire.

While of course one can see the bubble emerging in AI startups, and AI applications, this bubble may not ultimately prove as harmful as a purely financial bubble (eg the 2008 Global Financial Crisis) did. Some good may come even out of AI investments in companies that may ultimately have to be wound up before turning profitable. Some knowledge generation and diffusion will indeed occur, perhaps some patents will be filed or even approved, and ultimately some value may accrue to the investor, though perhaps not at the scale originally envisaged

The Evolution of Global Capability Centres (GCCs)

.@PhadnisShilpa & @sujitjohn write in the Economic Times that #GlobalCapabilityCentres set up by Multi-national companies in India initially in the 1990s, have transitioned from having business models based on cost arbitrage centers to #Engineering R&D value creators; developing, for example, generic malware signature generators (including by using Generative Adversarial Networks in #MachineLearning) to address issues in #Cybersecurity. The total employment in such GCCs located in India is now estimated at 1 million, with the overall value created of $28 billion.